{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collaboration and Competition\n",
    "\n",
    "---\n",
    "\n",
    "You are welcome to use this coding environment to train your agent for the project.  Follow the instructions below to get started!\n",
    "\n",
    "### 1. Start the Environment\n",
    "\n",
    "Run the next code cell to install a few packages.  This line will take a few minutes to run!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mtensorflow 1.7.1 has requirement numpy>=1.13.3, but you'll have numpy 1.12.1 which is incompatible.\u001b[0m\r\n",
      "\u001b[31mipython 6.5.0 has requirement prompt-toolkit<2.0.0,>=1.0.15, but you'll have prompt-toolkit 3.0.4 which is incompatible.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!pip -q install ./python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The environment is already saved in the Workspace and can be accessed at the file path provided below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:unityagents:\n",
      "'Academy' started successfully!\n",
      "Unity Academy name: Academy\n",
      "        Number of Brains: 1\n",
      "        Number of External Brains : 1\n",
      "        Lesson number : 0\n",
      "        Reset Parameters :\n",
      "\t\t\n",
      "Unity brain name: TennisBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 8\n",
      "        Number of stacked Vector Observation: 3\n",
      "        Vector Action space type: continuous\n",
      "        Vector Action space size (per agent): 2\n",
      "        Vector Action descriptions: , \n"
     ]
    }
   ],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "\n",
    "from maddpg_agent import MADDPG\n",
    "import torch\n",
    "from collections import deque\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "env = UnityEnvironment(file_name=\"/data/Tennis_Linux_NoVis/Tennis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Environments contain **_brains_** which are responsible for deciding the actions of their associated agents. Here we check for the first brain available, and set it as the default brain we will be controlling from Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Examine the State and Action Spaces\n",
    "\n",
    "Run the code cell below to print some information about the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of agents: 2\n",
      "Size of each action: 2\n",
      "There are 2 agents. Each observes a state with length: 24\n",
      "The state for the first agent looks like: [ 0.          0.          0.          0.          0.          0.          0.\n",
      "  0.          0.          0.          0.          0.          0.          0.\n",
      "  0.          0.         -6.65278625 -1.5        -0.          0.\n",
      "  6.83172083  6.         -0.          0.        ]\n"
     ]
    }
   ],
   "source": [
    "# reset the environment\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "\n",
    "# number of agents \n",
    "num_agents = len(env_info.agents)\n",
    "print('Number of agents:', num_agents)\n",
    "\n",
    "# size of each action\n",
    "action_size = brain.vector_action_space_size\n",
    "print('Size of each action:', action_size)\n",
    "\n",
    "# examine the state space \n",
    "states = env_info.vector_observations\n",
    "state_size = states.shape[1]\n",
    "print('There are {} agents. Each observes a state with length: {}'.format(states.shape[0], state_size))\n",
    "print('The state for the first agent looks like:', states[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Take Random Actions in the Environment\n",
    "\n",
    "In the next code cell, you will learn how to use the Python API to control the agent and receive feedback from the environment.\n",
    "\n",
    "Note that **in this coding environment, you will not be able to watch the agents while they are training**, and you should set `train_mode=True` to restart the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total score (averaged over agents) this episode: -0.004999999888241291\n",
      "Total score (averaged over agents) this episode: -0.004999999888241291\n",
      "Total score (averaged over agents) this episode: 0.04500000085681677\n",
      "Total score (averaged over agents) this episode: 0.09500000160187483\n",
      "Total score (averaged over agents) this episode: -0.004999999888241291\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):                                         # play game for 5 episodes\n",
    "    env_info = env.reset(train_mode=False)[brain_name]     # reset the environment    \n",
    "    states = env_info.vector_observations                  # get the current state (for each agent)\n",
    "    scores = np.zeros(num_agents)                          # initialize the score (for each agent)\n",
    "    while True:\n",
    "        actions = np.random.randn(num_agents, action_size) # select an action (for each agent)\n",
    "        actions = np.clip(actions, -1, 1)                  # all actions between -1 and 1\n",
    "        env_info = env.step(actions)[brain_name]           # send all actions to tne environment\n",
    "        next_states = env_info.vector_observations         # get next state (for each agent)\n",
    "        rewards = env_info.rewards                         # get reward (for each agent)\n",
    "        dones = env_info.local_done                        # see if episode finished\n",
    "        scores += env_info.rewards                         # update the score (for each agent)\n",
    "        states = next_states                               # roll over states to next time step\n",
    "        if np.any(dones):                                  # exit loop if episode finished\n",
    "            break\n",
    "    print('Total score (averaged over agents) this episode: {}'.format(np.mean(scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. It's Your Turn!\n",
    "\n",
    "Now it's your turn to train your own agent to solve the environment!  A few **important notes**:\n",
    "- When training the environment, set `train_mode=True`, so that the line for resetting the environment looks like the following:\n",
    "```python\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "```\n",
    "- To structure your work, you're welcome to work directly in this Jupyter notebook, or you might like to start over with a new file!  You can see the list of files in the workspace by clicking on **_Jupyter_** in the top left corner of the notebook.\n",
    "- In this coding environment, you will not be able to watch the agents while they are training.  However, **_after training the agents_**, you can download the saved model weights to watch the agents on your own machine! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "agent = MADDPG(seed=2, noise_start=0.5, gamma=1.0, t_stop_noise=30000)\n",
    "n_episodes = 6000\n",
    "max_t = 1000\n",
    "scores = []\n",
    "scores_deque = deque(maxlen=100)\n",
    "scores_avg = []\n",
    "\n",
    "def maddpg_train():\n",
    "    for i_episode in range(1, n_episodes+1):\n",
    "        rewards = []\n",
    "        env_info = env.reset(train_mode=False)[brain_name]    # reset the environment    \n",
    "        state = env_info.vector_observations                  # get the current state (for each agent)\n",
    "\n",
    "        # loop over steps\n",
    "        for t in range(max_t):\n",
    "            # select an action\n",
    "            action = agent.act(state)\n",
    "            # take action in environment and set parameters to new values\n",
    "            env_info = env.step(action)[brain_name]\n",
    "            next_state = env_info.vector_observations\n",
    "            rewards_vec = env_info.rewards\n",
    "            done = env_info.local_done\n",
    "            # update and train agent with returned information\n",
    "            agent.step(state, action, rewards_vec, next_state, done)\n",
    "            state = next_state\n",
    "            rewards.append(rewards_vec)\n",
    "            if np.any(done):\n",
    "                break\n",
    "\n",
    "        # calculate episode reward as maximum of individually collected rewards of agents\n",
    "        episode_reward = np.max(np.sum(np.array(rewards),axis=0))\n",
    "        \n",
    "        scores.append(episode_reward)             # save most recent score to overall score array\n",
    "        scores_deque.append(episode_reward)       # save most recent score to running window of 100 last scores\n",
    "        current_avg_score = np.mean(scores_deque)\n",
    "        scores_avg.append(current_avg_score)      # save average of last 100 scores to average score array\n",
    "    \n",
    "        print('\\rEpisode {}\\tAverage Score: {:.3f}'.format(i_episode, current_avg_score),end=\"\")\n",
    "    \n",
    "        # log average score every 200 episodes\n",
    "        if i_episode % 200 == 0:\n",
    "            print('\\rEpisode {}\\tAverage Score: {:.3f}'.format(i_episode, current_avg_score))\n",
    "            agent.save_agents()\n",
    "\n",
    "        # break and report success if environment is solved\n",
    "        if np.mean(scores_deque)>=.5:\n",
    "            print('\\nEnvironment solved in {:d} episodes!\\tAverage Score: {:.3f}'.format(i_episode, np.mean(scores_deque)))\n",
    "            agent.save_agents()\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 200\tAverage Score: 0.000\n",
      "Episode 400\tAverage Score: 0.069\n",
      "Episode 600\tAverage Score: 0.082\n",
      "Episode 800\tAverage Score: 0.074\n",
      "Episode 1000\tAverage Score: 0.195\n",
      "Episode 1200\tAverage Score: 0.488\n",
      "Episode 1202\tAverage Score: 0.513\n",
      "Environment solved in 1202 episodes!\tAverage Score: 0.513\n"
     ]
    }
   ],
   "source": [
    "maddpg_train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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EdMstt6hDhw5q0qSJevTooRdffDG83xijpUuXasqUKUkrebEWLlyoM888Uy1bttQhhxyigQMH6v3330/rGk2ePFlPPPGEZs6cKWOMjDF68803XR/nxBNP1F133aWLLrpI5eXlrsfcfffdGjdunC677DIde+yxuv/++/Wtb31L06ZNkyR9/fXXeuSRR3TnnXfqjDPOUO/evfXkk0/q448/1pw5cxI+x1TX2+l6+vTTT2vgwIEqLS3VMccco9mzZ8cd43RPrays1M9+9jO1bdtWTZo0UYcOHfSrX/0qfPxXX32lsWPH6vDDD1dZWZmGDh2q5cuXR7XrL3/5iyoqKlReXq4RI0Zo69atcW1/+eWX1adPH5WWlqpTp06aOHGiDh48GN7//PPPq2fPniorK1OLFi00ePBg1/sBAAANW+NGNYnRn3XGPK7TaIy5QNJ/S+or6UhJ6yQ9L+l31trdKc5N9HqcYK1dUq8NnXxovd5d7drwddqHzpgxQxUVFerZs6cuvvhijRo1SrfddpsaN24sSRozZox+97vf6c4771RRUfA7g+eee05lZWUaPjy4xtLYsWO1efNmzZs3T2VlZbr++uu1du3ajJq8aNEiXX311XriiSc0cOBA7dy5U/PmzQvvf+ihh/Tzn/9cv/vd7zR8+HDt2bMnvN9aq/POO0+lpaWaN2+ejDH66U9/qvPOO08LFy4Ml/+/+OILPf3003r22WdVUlKi0tJS3XTTTZoxY4YeeOABfec739H777+vyy67TIcffnj4+cW69957deedd+qPf/yj+vbtq+nTp+uHP/yhFi9erOOPP16bN2/WaaedphEjRmj8+PFq1qxZWtdg9+7duvjii3XvvffKGKM//OEP+v73v6/PPvtMLVu2THqNxo8frxUrVujLL7/Uk08+KUlq0aJFRq+B4+DBg1q8eLHGjx8ftf3MM8/Ue++9J0lavHixKisrdeaZNesJdejQQccee6zee+89DRs2zPW+073eN954o+6++2717NlTDzzwgM4991x9/vnnateuXdx93nfffXrhhRf0t7/9TR07dtSGDRu0atWq8P5x48Zp1apVevHFF3X44Ydr4sSJOuuss/Svf/1LZWVl+uCDDzRu3DjdcsstGjlypN544w1NmDAh6jFmzZql0aNH695779WgQYO0bt06XXHFFTpw4IDuuusubdmyRRdeeKFuu+02nX/++dqzZ48WLFiQ+cUHAAC+kKxHW0OYCCef/cHGKxgUJ0jaIOkESZMlfdcYc7K1NpDkXEl6XNJDMdv+Vc9t9JyHH35YF198sSRp8ODBKi8v10svvaTzzz9fknThhRfqF7/4hd544w0NGTJEkvTUU09p5MiRKikp0apVqzRr1iy9//776t+/vyTp8ccfTzj5SyLr1q0Lj4tr3ry5Kioq1KtXr/D+W265Rddee62uu+668LY+ffpIkubMmaOlS5dq9erV4cd9+umn1aVLF82dO1dDhw6VFAxDTz75pFq3bi1J2rt3r+6++27Nnj1bp556qiSpU6dO+vDDD/XAAw8kDI133XWXxo8fr4suukiSNGXKFL399tu66667NH369HDXyWbNmqlNmzZpX4PTTz896u/7779fzz33nF577TWNGTMm6TVq1qyZysrK1KRJk4we082OHTtUXV0dvk6O1q1bh6uIW7ZsUXFxsVq2bBl3zJYtW1zvN5PrfeWVV2rUqFGSgiF91qxZmjZtmqZOnRp3v2vXrlXXrl116qmnyhijo446SieffLIk6bPPPtNLL72kt956S4MGDZIkPfnkkzrqqKP01FNP6dJLL9W9996rIUOGaOLEiZKkrl27auHChVFV91tvvVU33HCDfvSjH0mSOnfurDvuuENjxozRnXfeqU2bNqmyslIXXHCBKioqJIlJjgAAgKuSyIlwfFpqzGf31LOttaOstU9Za9+y1v6fpJ9J6ifptDTO32itXRDzsy+rLS5wn3/+ud59991w+DHGaPTo0Xr44YfDxxxxxBEaNmyYnnrqKUnS5s2b9cYbb2jMmDGSgrOEFhUVqW/fvuFzOnTooLZt22bUljPOOEMVFRXq1KmTRo8erSeeeEK7dwcLyNu2bdPGjRvDoTXWihUr1LZt26igevTRR6tt27b69NNPw9vat28fFYQ+/fRTffPNNzrrrLPUrFmz8M+0adO0evVq18fatWuXNm3aFDdp0MCBA6Meqza2bdumn/zkJ+ratasOPfRQNW/eXNu2bQuPI0x2jbIhdvZQa23KGUWTHZPJ9R4wYED496KiIvXr1y/h9R03bpyWLFmirl276uqrr9bMmTMVCAS/Q1qxYoWKioqi7u/QQw9Vjx49wve3YsWKqP2xjy8FK6u33nprVLsvuugi7d27V1u2bFGvXr00dOhQHXfccTr//PM1bdo0bd++Pem1AgAA/pUsCzZuVCS/D2rMW6XRWuv2CcyZljK+zxpSevjhh1VdXa2jjjoqvM0ppa9fv14dOnSQFOyievnll+vBBx/UX//6V3Xo0EEDBw6MOr6umjdvrn/+8596++239frrr+u2227ThAkTtHDhQhUXFyc9N1lQidzetGnTqH1OsHj55ZejroGkcPfcRNwer65LNIwdO1Zbt27VPffco44dO6pJkyYaMmRIeNxcsmuUaUhPpmXLliouLo6rGG7bti0cutu0aaPq6mrt2LEjaqKfbdu2hSt6sepyvZPp3bu31qxZo9dee03z5s3T2LFj1atXL73++utJ35+ZzFoWCAQ0adIkjRw5Mm5fq1atVFxcrNmzZ2vBggWaPXu2HnnkEf3617/WW2+9FVUxBwAAiKo0+nRUY6FNhDM4dJvOlJ9XGmMOGGP2GWPmGWNOzWbDCl1VVZWeeOIJ3XbbbVqyZEn4Z+nSperZs6cee+yx8LHnnnuuJOmVV17RU089pdGjR4c/cB977LEKBAJavHhx+PgNGzZo06ZNGbepUaNGOv3003Xbbbfp448/1t69e/XKK6+odevWateunebOnet6Xrdu3bRx40atWbMmvO3f//63Nm3apG7duiV8vG7duqlJkyZau3atunTpEvXjdDGMdcghh6ht27Z65513ora/8847SR8rHe+8846uueYaDR8+XN27d1fz5s21efPmqGMSXSNJKikpqZeZS0tKStSnTx+9/vrrUdtff/31cLfPPn36qHHjxlHHbNiwQStWrAgfEyuT6x05HtBaqw8//FDHHntswjY3b95cI0eO1LRp0zRz5kzNmzdPn3/+ubp166ZAIBA1odCuXbv0ySefhF+vbt26xY0/jP27d+/eWrlyZVy7u3TpEp7F1RijAQMGaNKkSeEg/8wzzyRsMwAA8K9k30mXNGJMY84YY9pJmiJpjrU21Srf0yW9ImmTpApJN0iaZ4w5w1r7ZpLHuFzS5ZLiKiMJZTAJTT7NnDlTO3bs0GWXXaYjjjgiat+FF16oadOm6aabblJRUZFKS0v1wx/+UFOnTtXSpUujZrv8zne+o2HDhumKK67QtGnTVFpaqhtuuEHl5eVRlbdLLrlEUnCWSjevvPKKVq9erUGDBqlFixZ64403tHv37nBQmDhxon7xi1+odevWGj58uPbt26e5c+fq+uuv19ChQ9WrVy+NHj1a9913n6y1uuaaa9S7d++4cYKRmjdvrvHjx2v8+PGy1mrQoEHhCUyKiop0+eWXu553ww036Oabb9a3v/1t9enTR9OnT9f8+fOjgnNtdO3aVdOnT1e/fv20d+9e3XjjjSopKUn7GnXs2FH/+Mc/tGrVKh1xxBE69NBDXSt4Bw8eDHfN/Oabb7RlyxYtWbJEzZo1U5cuXSRJ1113nS6++GKddNJJOuWUU/THP/5RmzZt0hVXXCEp2MXzxz/+sW644QYdeeSROuKII3TdddepZ8+e4TGksTK53tOmTVPXrl3Vo0cPPfjgg1q7dq2uvPJK1/u9++679a1vfUvHH3+8GjdurKefflqHHHKI2rdvr/Lycp177rn6yU9+oj/96U867LDDNHHiRB1yyCHhbtk/+9nPdPLJJ+u2227TBRdcoDfffDM8M7Dj5ptv1ogRI1RRUaFRo0apUaNGWrZsmT788EP9/ve/14IFCzRnzhwNGzZMrVu31kcffaT169fX+YsEAADgP40jKo0+LTQGv/XP94+kZpIWKRgC29fi/OaS1kp6J91z+vTpYxP59NNPE+4rVGeffbY944wzXPetXr3aSrKzZs0Kb5s7d66VZHv37h13/ObNm+2IESNskyZNbIcOHexjjz1mjz76aHv77beHjxk8eLAdPHhwwvbMnz/fnnbaabZFixa2tLTUdu/e3T766KNRxzz88MP22GOPtY0bN7atW7e2P/rRj8L71q5da88991zbrFkz26xZM3veeefZ9evXh/dPmjTJdu/ePe5xA4GAve++++yxxx5rS0pKbMuWLe3QoUPt7NmzE7a1urraTpkyxbZv3942btzYHnfccfaFF16IOqZ79+520qRJCe/DrU1LliyxJ510ki0tLbVHH320/ctf/hJ1P6mu0bZt2+wZZ5xhmzVrZiXZN954w/Vxv/jiC6vgf6KifmJfnwceeMBWVFTYkpIS27t3b/vWW29F7d+/f7/96U9/alu0aGHLysrsiBEj7Lp165I+51TX22nb9OnT7YABA2yTJk1s165d7auvvhrX/oULF1prrf3Tn/5kTzjhBNusWTPbvHlzO2jQIPvuu++Gj//yyy/tJZdcYg877DBbWlpqhwwZYpctWxbVrkcffdR26NDBlpaW2rPOOsvef//9NvifuxqzZs2yAwcOtGVlZbZ58+a2T58+9v7777fWBv8bcNZZZ9kjjzzSlpSU2M6dO9s77rgj4XXw4n8zAKA+VPzyFVvxy1fy3Qwg63buPRh+v8f+LNu4005fsMZW/PIVu+Xr/Z76dyFpkU0jOxmb5yl+jDGlkl6VdLykwdbaT2p5Pw9K+rG1tkk6x/ft29c668LFWrFiRdKucw3Njh071LZtW/31r38Nz8IKpGPNmjXq1KmTFi5cGDW5kt/w3wwADVXHX82UJK253X2GcsAvvt5XqV5TZrvum3PdIH34xVea8MIn+mDCEPX7XXAIlhf+XRhjFltrU35Iy2v3VGNMY0nPSTpJ0tDaBkbn7uTfgnBOzZs3T7t371aPHj20bds2TZw4US1bttRZZ52V76YBAAAAOZdsgpvGDWDJjbyFRmNMkaSnJA2RNNxaW+uVs40xh0gaLumDempeg1ZZWambbrpJ//73v1VeXq5+/frp7bffjputFAAAAGjoGhcX+X3FjbxWGh+QNFLSrZL2GmP6R+zbYK3dYIypkLRa0hRr7RRJMsaMl/QdSW+oZiKc8ZLaSBqdw/b71rBhwzRs2LB8NwM+0LFjx3pbxgUAACBfUs2eGj7Opx0f8xkavxe6nRj6ifRbSZMV7HJarOilQVZJ+kHo51BJuyS9q+B4xg+z2F4AAAAAiNK4mCU3ssZa2zGNY9ZI0a+BtfZlSS9np1UAAAAAEC1Z/bCkAYxpLEp9SMMUCATy3QQAHkD3WwAAGraSRv4f00hodNG0aVNt3LhRBw8e5AMhgISstfrPf/6j0tLSfDcFAABkUbJMUFxUkxj9mhzyuuRGoWrfvr127NihtWvXqqqqKt/NAVDASktL1b59+3w3AwAA5JHx+ahGQqOLoqIiHXnkkTryyCPz3RQAAAAAeZZuBdGvvRTpngoAAAAAdREqNPo0MxIaAQAAACCZRGHw+z3aSJLPO6cSGgEAAACgVq7+bpd8NyEnCI0AAAAAkIRNMKrRmQDHhNbcCPi0fyqhEQAAAABqIXZ9Rp9mRkIjAAAAACSVIAw6odHJjlQaAQAAAAAJ+TMyEhoBAAAAIKlEYbBmTGPoOCqNAAAAANDwJMqCjGkEAAAAAKTkhMcAoREAAAAAGp7ES244tyy5AQAAAACIQfdUAAAAIA1+nfwDcCR+i0dPhEOlEQAAAADQ4BAaAQAAUCc+La4AYQmX3IjpnkqlEQAAAAAQFpMZffsFCqERAAAAdeLTz8lAWKJxu8aYqFsqjQAAAACAhPwZGQmNAAAAqCNmT4XfJXqLm5hbv/5bIDQCAADnTX0RAAAgAElEQVQAQC2wTiMAAACQBp9+TgZSMnHrNOaxMVlEaAQAAACAOnDCIxPhAAAAAC58+jkZCEs4ppHuqQAAAACAVJzwyEQ4AAAAgAvLqEb4XKL3eFylMQdtyQdCIwAAAADUgZMdY8c0VgesNu3cr70HqnLfqHpEaAQAAECd+LRHHhCWeEyjSXrcf/Yc0Mm3z9Pfl2zMUstyg9AIAAAAALXgRMaaJTeiU6Nfvk8hNAIAAABAEonCX6oxjU6GNIo50GMIjQAAAABQJ8FQGDt7qjOBTmy49BpCIwAAAOqEMY3wu0RLaTgVxHD31ID7+R7PjIRGAAAAAKiNdLuneh2hEQAAAHXCOo3wu4RjGmNuE02EQ/dUAAAAAEBcZdHp1spEOAAAAGjQ/NIFD0gk4Xs8lAVr1mtMtKBjfbcotwiNAAAAAFALsRXE+EpjDhuTRYRGAAAA1IlPPhcDSSSYPdWpNIb+Dviz0EhoBAAAAID6EDsplFNpNB6fCYfQCAAAgDpJtIYd4BeJ3uLh2VOddRqpNAIAAAAAHLEVxNgvUPyyHA2hEQAAAHXij4/FQGIp12kM/ZJoIhyP904lNAIAAABAXTizqAbiKo2h/YRGAAAANGQMaYTfJRzTaNI8zuOjGgmNAAAAAFAL4TAYnggndvZUf3yjQmgEAABA3fjjczGQUMIJbWIrjXHnhQ7zdqGR0AgAAAAAdeFkwrjZU33yhQqhEQAAAHXil2UFgETqOqbR6/IWGo0xFxhjnjPGrDXG7DfGrDLG3GaMaZ7GuaXGmDuNMZtD575vjBmUi3YDAAAAgBS55IYze2rsETZqv1fls9I4XlK1pAmSzpI0TdKVkl43xqRq1yOSLpN0s6QRkjZLmmWMOT57zQUAAIAbv1ZXAEfiSmN0GIytuofXacxGo3KoUR4f+2xr7faIv98yxnwp6QlJp0ma53aSMaaXpIsk/Y+19rHQtrckLZc0RdI52Ww0AAAAAERyQmF8pTG03+OpMW+VxpjA6FgYum2X5NRzJFVKeibivqok/U3SMGNMk3prJAAAAFKi0Ai/SzRut6Z7aui42IlwstimXCq0iXAGh25XJDmmu6QvrLX7YrYvl1QiqUs2GgYAAAAAkVJNhFPTPdXbpcaCCY3GmHYKdi+dY61dlOTQFpK+ctn+ZcT+RI9xuTFmkTFm0fbtboVOAAAAZMovC5gDiaR6izuhMBBXaXQmwslKs3KmIEKjMaaZpBclVUn6UarD5V7pTflSWGv/ZK3ta63t26pVq8wbCgAAAAAhsRXEhBPm5KAt2ZT30GiMKZX0kqSjJQ2z1m5IccqXcq8mHh6xHwAAADlCnRENlVNBdG7jKo0++ceR19BojGks6TlJJ0n6vrX2kzROWy6pkzGmPGZ7N0kHJX1ev60EAAAAgMyFxzR6vNSYt9AYWovxKUlDJJ1rrV2Q5qkvSWosaWTEfTWS9F+SZltrD9R3WwEAAJCYX6opQCKpxzQGJRrT6PUOqvlcp/EBBYPfrZL2GmP6R+zbYK3dYIypkLRa0hRr7RRJstYuMcY8I+n/QpXKLyRdKamTpNE5fQYAAAAAGqxUs6cmOs5r8tk99Xuh24mS3o/5uTS0z0gqVnw7fyTpMUlTJc2U1EHSWdbaf2a5zQAAAIiRaA07wC8Sr9PoDGoM3gQSLLnhdXmrNFprO6ZxzBq51HKttfslXRf6AQAAAICci6s0JgyX3pb32VMBAADgcT6ppgCJpLtOY+xxNRPheDs2EhoBAAAAoBacKBheciO2f2rMcV5FaAQAAECdUGiE3yV6j8dWEGOP88t4X0IjAAAAANSCibmNW3KDdRoBAAAA/8wQCSRi03yTx41pDN0SGgEAAACgAXLCoNNNNVG4NB4f1UhoBAAAQJ34ZdwWkEitxzT6pAxPaAQAAACAOgjPnho7pjF8QE6bU+8IjQAAAKgTnxRTgITSfY8nXKexfpuTc4RGAAAAAKiDmtlTE+z3+Ew4hEYAAADUCYVG+F/yd7mTCePH9/rjXwehEQAAAADqA91TAQAAgHh+mSESSCT1WzwYCxNNhOPx3qmERgAAAACoD4nCJes0AgAAoEGj0Ai/S/UWr1lyI+Y8n/zbIDQCAAAAQD2InQjH6bpN91QAAAAA8LFUFUMnE8at0xiz36sIjQAAAKgTv3TBy6aJL3yi++Z+lu9mIMsSTgrl8dTYKN8NAAAAAPzuqQ/WSZJ+NuTbeW4JaiPVDMHGOLOnxp6XrRblFpVGAAAA1En8guZAwxQ3pjH0N7OnAgAAAICPpZw9NXQbW2l0TmQiHAAAADRofumCB9SWEwoTdWP1eGYkNAIAAABAMul+MZJo9lSvIzQCAACgTvzywRioLWfMYsDGrtMY2u/x/qmERgAAAABIIt3JnuIrjaGJcLydGQmNAAAAqJtUyxEAfueEwriJcJz9uWtKVhAaAQAAACCZdMc0xi654ZPvUwiNAAAAqBOffC4G6izRRDh0TwUAAAAAH0v3i5HYrto1f3s7NRIaAQAAUCd+6YIH1FbKMY3ezoyERgAAAABIJt0vRuKW3MhCW/KB0AgAAIA68stHY6B2nHUa4/4lOOs05rQ19Y/QCAAAAABJpFqn0el+GjemMbxOo7djI6ERAAAAdcKYRiAo0b8Fb0dGQiMAAAAAJJXqi5GaiXBYpxEAAACI45PPxUBC6S+54f63x3unEhoBAAAAoC6ciXBil9ywMfu9itAIAACAOvFLFzwgkdgJbpIc6bqVSiMAAAAANGA1Yxqjt6cfNgsboREAAAB1kmo5AsDr0h/TGLvkhj8QGgEAAACgDpzep/GVxtB+uqcCAACgIfNJDzwgsVouuRHez0Q4AAAAAIB4/vhGhdAIAACAOqHSCL9LPW7XWXIjZkwj3VMBAAAAAI7YL1DC6zQSGgEAANCQMXsq/C5VNT3VmEavIzQCAAAAQD2IqzQ63VOZCAcAAAANmU+LK0BYykqjc1zseaEtdE8FAAAAACScLNXjmZHQCAAAAADJpJw71SSfPdXrCI0AAAAAUA+c0Oh0R2X21HpgjGlvjLnfGPO+MWafMcYaYzqmee6a0PGxP+dlt9UAAACI5JdqCpCITfEmTzimMXyet1Njozw/fhdJoyQtljRf0pkZnj9L0uSYbavq3iwAAAAASE/NkhvJ93tVvkPj29ba1pJkjLlUmYfGHdbaBfXfLAAAAKSLdRrhd2m/w53uqVlrSX5k3D3VGNPcGHOzMeYdY8xnxpgBoe0tQ9uPSfe+rLWBTB8fAAAAAAqJsw5jbKWxZp1Gb8soNBpjWklaJOk3ko6QdLSkMkmy1u6QNFbS5fXcxmTODo2FPGCMWcB4RgAAgNxjTGP6Fq/9Kt9NQC2k+x6vWZfRuP7tVZlWGqdKaiOpn6RTFR+aX5Q0pB7alY6XJV0jaZik0ZK+kfSCMWZMohOMMZcbYxYZYxZt3749R80EAAAAgs6f9l6+m4AsCI9pTNCP0tuRMfPQOELSg9baf8q9a++/JXWoc6vSYK29xlr7F2vtfGvtDAXD6iJJtyU550/W2r7W2r6tWrXKRTMBAAB8j0Ij/C+9d3nsXKl+qcJnGhpbSvo8yf6ApNLaN6f2rLXVkp6V1N4Y8618tAEAAABAwxW7NEd4TKPHS42ZhsYtkjon2X+CpHW1b06dJVoiBQAAAFmSag07wOvSHtMYOxFO6NZ4vINqpqHxVUk/dqvkGWP6SbpEwXGNOWeMaSRppKR11tot+WgDAAAAgIanZp1GG/V37H6vynSdxt9KOkfSR5JeUjA8jzXGXCbph5I2Sbojkzs0xlwQ+rVP6PZ7xpjtkrZba98KHVMl6Qlr7Y9Df/+3pHMVDLHrJbWWdHXoPv47w+cEAACAOqDOCL9L9R53ZkcNxHVP9ce/joxCo7V2izGmv6Q/SPofBbuDXqzgdXxV0pXW2i8zbMOzMX8/GLp9S9Jpod+LQz+OLyQdKelOSS0k7ZO0UNJZ1tpZGT4+AAAAkDPWWs8vwQB3sd1R/REZM680ylq7XtK5xphDJH1HweD4eS3ConN/Kf/FxB5jrV0g6fTaPB4AAADql0+KKTkTsFIxmdFTUr3HE86W2tAmwjHGNDPGPGqMGSlJ1tpd1tqF1toPaxsYAQAAgIbGL10WES/82saNafR2akw7NFpr90i6UNIh2WsOAAAAvIcQlIkAl8tzbIr3eM1EOJmd5xWZzp76qaSOWWgHAAAA0CDETpYC/4gNiQkKj56TaWj8vaQrjTFds9EYAAAAeA8ZKDNcL+9JPaYxNHtqwPk7dJ6z3+OpMdOJcI5RcImLT4wxr0j6TMGZSyNZa+0t9dE4AAAAwG+oNDY8xuO1xkxD4+SI33+Q4BgridAIAADQQBCBMsP18p7U6zQGb50vBJy//fL9QKahsVNWWgEAAAA0EFQa/Sv2tXXGODao7qnW2rXZaggAAAC8iQyUGRvIdwuQqVTLpMSu0+h0R/XLRDiZVhrDjDFHqKby+IW19j/10yQAAADAP2IDB5VGH0qw5Ebsfq/KdPZUGWN6GWPekrRN0gehn23GmDeNMT3ru4EAAAAobCxWn1zs5SE0+lls91R/yKjSaIw5TtI7kkolvSRpWWhXd0lnS5pvjDnZWru8XlsJAAAAeFRscPBLkECN8JIboRc3vF6jMzGOx0uNmXZPnSKpUtLJ1tpPIneEAuXboWPOr5/mAQAAoNARgpKje6r3pfuSxb7WflmnMdPuqYMkPRAbGCXJWrtM0oOSBtdHwwAAAAA/iKs0khl9x8SMaYx9jT2eGTMOjU0lbUmyf3PoGAAAADQQhKDkGNPofTbNerpTabThv7PUoBzLNDT+W9KIJPtHhI4BAAAAoPjAkXCGTXhW7JIbCv/trNPo7VpjpqHxL5KGGWOeNsZ0N8YUh36OM8Y8JelMSY/XeysBAABQsNKtwjRUiYIEvCPtMY0xv4THNNZze3It04lw7pLUW9KFkv5LkrM0aZGC1+L/SfrfemsdAAAA4DNkRv9xKomJuh57vNCYWWi01lZL+i9jzMOSzpPUScGwuFrS3621c+q/iQAAAChohKCMMKbRe1K9ZE4mDITHNNq0zvOKTCuNkiRr7euSXq/ntgAAAAC+Ez8RTn7ageyL64ocuvX6Oo0ZjWk0xrQwxvRMsr+nMebwujcLAAAAXkEGSi5+IhyumNekesWc7qfOS1tzG/rF25kx44lwfq/kE908Jum2WrcGAAAA8Bkmwmk4wt1SY7Z7fUxjpqHxu5JeTrL/JUlDa98cAAAAeA0ZKLnYy8P18p5UQd/pfurXrseZhsa2ktYl2b8hdAwAAAAaCJbcSC42cPg1WKDmta65DW73eKEx49C4V1JFkv0Vkg7UvjkAAACAv8RmRMY0ek/KVyyUCmO/EHC+UDEe75+aaWj8QNJYY0zz2B2hbZdI+rA+GgYAAABvIAMlFz97KhfM7+LGNOalFfUn09B4l6T2kt4zxlxgjOlijOlsjLlA0nuhfXfWdyMBAAAAz4qbCCc/zUAdpFqnMUEq9MtrndE6jdbaN4wxV0m6V9IzEbuMpIOSfmqtnVOP7QMAAECB88nn4qyJHfPplyCBxMJLboT+9njv1MxCoyRZax8yxrwiaZSkLgoGxlWSZlhrN9Zz+wAAAABfoXuq96Sa7ClRJqyZCMfbqTHj0ChJoXB4jzGmkaSTJLWTdJgkQiMAAEADw7qDyTGm0f9STXTj9UpjyjGNxpjTjDH3GWPaxGzvKGmxpPmS/ibpY2PMo9loJAAAAOBV8bOn5qUZqIPa5HxrrW+Wo0lnIpxxks611m6J2f4XST0UnADnHkmfKjiz6th6bSEAAAAKmj8+FmdPbCWWyqz/pOqe6nXphMYTJb0cucEYc4ykgZLettaeaq0dr2A31c8UXHYDAAAAgOJDtU9yRINSm9csMjD6vnuqpG9J+lfMttMUvHYPOxustfslPS2pZ301DgAAAB5ACkoqbkwj/VN9xy0URr7KXp8IJ53Q2ETS/phtJ4Zu34rZvl7SoXVtFAAAAOAXsePayIzeU9tupn7pipxOaFwnqXvMtoGStllr18dsL5e0sz4aBgAAAG/wy2QfWRNzefwSJFDDrZJora1ZcsPbhca0QuN8SZcYY3pIkjHmB5K+LekfLsf2EMtuAAAAAGHMnup9tf1ixDnL45kxrdB4m4JdVJcYY7ZJmiHpoKT/jTzIGFMs6RxJ79R3IwEAAFC4KJwlF3t9qMz6T8oxjR4vNaYMjdbaLyQNlvSqpP8oWGE8zVq7PObQ74b2v1jfjQQAAAD8gkqj99R+TGP9tiNfGqVzkLV2kaSzUxwzR8HuqQAAAGhA/PLBOFviJ8LhgjUE1ta89t6uM6bXPRUAAABALcV1TyU0ek6qV8y9e2rDmggHAAAASIgIlFzcRDiBvDQDeeT7MY0AAAAAai+2skjI9qAU1WH3JTf881oTGgEAAFAndLdMLvbyMKaxAfHJa01oBAAAAHKIkO09tRnTmM4+ryA0AgAAoE6IQMnFVxrz0w7knl9eakIjAAAAkEUsueF9qV4yt2KitcEfHxQaCY0AAACoGzJQcvFLbuSnHcgtG/qf12dOlQiNAAAAQFbFLblBavScVONQkwVD70dGQiMAAADqjBCUCTKj/yTrnuoHhEYAAAAgi2KrVFQavae2r5gVs6cCAAAAvqmmZEt899S8NANZ5BYMrZyJcLyfGvMaGo0x7Y0x9xtj3jfG7DPGWGNMxzTPLTLG/NoYs8YY840xZqkx5vzsthgAAADITPySG6RGr6nNSxauMHs/M+a90thF0ihJX0man+G5t0iaLOkPkr4naYGkZ40x36/PBgIAACA5IlAqsdOn5qcVyJ5EE+HELrfiVY3y/PhvW2tbS5Ix5lJJZ6ZzkjHmSEnjJd1urb0rtPkNY0wXSbdLejUbjQUAAAAyRaXR+2rzitnQ//mg0JjfSqO1NlDLU4dJKpE0PWb7dEk9jDGd6tQwAAAApI0MlBxjGhsuJsLJr+6SDkj6PGb78tBtt9w2BwAAALm0cM2Xqqqubf0htwq10vhNZbWWrN8Zta2yOqDFa7/MU4sKV6p1Gt3PCd4yEU7+tJC008a/el9G7I9jjLncGLPIGLNo+/btWW0gAABAQ5HrcVv/XPeVRv7xfd0797OcPm5txV6f2gSQbLhhxsc674F3tW33N+Ftcz7dqvOnva/NX+/PY8u8yXUG1QJ5revKq6HRyL1rcdIYb639k7W2r7W2b6tWrbLTMgAAAGTVtl0HJEmrtuzOc0vSE5sbCiVGfLwhWGXce6A6vG3Pgaq4bUhPXBCxoSU3vF9o9Gxo/FLS4SZ+mqLDI/YDAAAgB3xSTMmZQIEManTLMtWhtlUFvNH1N1dqteRGqMbsg8zo2dC4XFITSZ1jtjtjGT/NbXMAAAAAd/FjGvPTjlhO/SWyC2V16Peq6gJppIckWnYj0XYv8WpofE3SQUmjY7aPkbTMWvtF7psEAADQMBEvkosd01goE+E4USayNU4VtLpQkm2BqM24XWv9U4XP9zqNMsZcEPq1T+j2e8aY7ZK2W2vfCh1TJekJa+2PJclau80Yc4+kXxtjdkv6p6T/knS6pHNz+gQAAACAJOLGNBZKkAilxsj21HRPLZRGeodbPdHK+qJ7at5Do6RnY/5+MHT7lqTTQr8Xh34iTZS0R9LPJbWRtErSKGvty9lpJgAAANz4ZYbIXMn1bLOJ1ISZyO6poVtCY5TajWkMneeD1Jj30GitTXkZ3Y6x1lZLmhr6AQAAAApSoY5pdES2z+me6pU1MAtJoqGLPsiMnh3TCAAAAHhCwY5pdCbCidhWRfdUV7W5Gn6qwBMaAQAAgCwq1DGN4YlwIiuNlolwasvE1BSD3VMts6cCAAAAhRKCClXs5SmYdRqdiXAixzRSaXRV2/e4VeJuq15CaAQAAACyKLabYqHEMacyFogYvlgdXnKDMY0ZiwmHzsvug8xIaAQAAEDdFMpsoF7x6LtfRE008/a/tmvZxq9z3g6nAhY5xtL5vbLaav5n2/XJhpp27dx3UE9/sC6nbSwU6bzHXZfc8Mk/DUIjAAAAMuanST6yLfZK7dxXqec/2hj++5JHP9SI+9/JbaMUMRGOyzqN1QGrix/5UGf/oaZd1/+/pZrwwif6dNOunLazEJ1w1GH6yeCjkx5jQ//zw5jGvC+5AQAAAG8jPybndn2qqgvnolXb9MY0bt9zQJJ0sAEuxxH7Gr5w1Slxx7hlQ2vpngoAAIAGiqCYifiLVV5SnId2RHPCTMAlNLqNafTTGL2ccK6XDy4YoREAAAB1QoBMzu36lBVCaHTGNEZUFasjxjSmOg/RXJfcyE9T6h2hEQAAABnzy4fhXHC7VgVRaQxPhFOzLRAxpjGWMxlMbDhCYsEM7v3rRWgEAABAnRAgk3OrNBYVQLnOCX/VLpVGtzGN1kfdLTOVzsRPsdcleIr1xfUiNAIAACBjzJ6aPrdrFSiA6+eEGRs1ptG5TTymEZnxQWYkNAIAAKBuch8gvZVe3FrrUsjLOSfMRM+eGgyLrpVG5zw/pKAMpfMWj70sVtY3QZvQCAAAgIz55LNwTrgFh4Ko1BqX7qmhAqPbkiBOmxnT6C52PUZrQ0tu+OByERoBAABQJ7mPP976FG5drlBBZMbQbWRbAknGNIbP89blrxe1fbmsrC9CNqERAAAAGSuE0ONlhTSmMbrSmHqdRriL754a2u79zEhoBAAAQB0RJpJzuT6FNKYxMsAmnT21Ab/QtQ3MfgnahEYAAABkLL8BwlufxN0nwsn/c3DG4EW2JbxOo+uYxuhbxIhbciP4r8QHhUZCIwAAAOqmIVeg0uE+EU7u2xGrptJYs82pMCabPbUQAm+u1fY9HpwIx/uxkdAIAACAjOU3N3jrQ3hk4DiqRXlwWwEEL7cxjYFwaHQb05j/NheyuDGNPrpchEYAAADUiZ8+HGdD5PUpCiWLwhjTGN891RnTWE2lMUqtxzT6pApPaAQAAIDHeOuDeGRri1zGEeZNOMDGz57qtk6j80QKoemFyLUbKus0AgAAALmPcF4LLZHdOp0AUQhPITymMaInarJ1Ght0pbE259jgeYRGAAAANEj5zA2F0LUzE26VxkIYHxge0+hWaXQLjaHjvHb9cyVRODQeG4PrhtAIAACAOsl1/vHcOLGoMY2F0z01PKYxaiKc4G2120Q4Lr81GGm8XnET4cgWxJcD9YHQCAAAgIzlM7h5+XO4U41yyWR5E73kRrBhbmManeveUCuNtelmSvdUAAAAQLkPkIVQpctE5PUppEpjUSgJRHVPDf3qPntqcFsBND3n0nnKsRPhWBtapzE7TcopQiMAAAAy1hCDQ21FLbkR+vRdCJfP6Z4a2YXS6apamaScWAiBNx8yDX/OVXKdVdVjCI0AAACok1xnCK+Fluh1GgtwIpyIgOj87jqmMdw9Nf9tz7V0nrITDYsiMqJfrhShEQAAABnL54dhr2UW93Ua89MWN5FtCS+5kWRMo2+SUIbSrRhGfjFgraV7KgAAAFCXDPHiko3qc8vrqqpOf2YYz4VGl3UaU1Xrfvr0P3XDs0uz2aywgGulMVn31Kw3qSBs/nq/uk78h5Zv+jqtcbvOa+vc/tefFgTP8kFqJDQCAAAgY1HdK+uQ4ia9tFz/2XtQu7+pSvscr3WPdF+nMbQvwXN55ePNenbxhqy2y7hMypPOOo2eW/Kklt5YuV0HqwOavmCtpPSzn3Ndt+8+kNF5hYzQCAAAAE/xWmSJHtPobAtuzGfVzgkz0bOnOqEx8TqNDaXS2Cj0YlVV2zS/FwkeHzmm0XNv1gQIjQAAAMiYTfB7Th7bY5VGuS65odBt/p6LCQfYmm3hSmOSMY3eu/61U+yExtA1STWk0dlfFHGglWX2VAAAACDXvJZZ3GZPdcJiPp9LuNIYUTp0xjcedBlj2tDWaWxUXBMaM3nKUaGRdRoBAADQUNXTkMaa+8jSsYWmZiIc5zaflcZgY6KW3Ehj9lSvjSmtrUahRTWdSZpMivhnYm7D232QGgmNAAAAyLtMgojXQov7RDiF8xxs1EQ4wdtK10qjc3wOGlUAIrunZvKcI0OiX64VoREAAACZi6o01v2TcSZB0GsfxKO6pxZFz56azwDsPHbURDihCXBcQ2MBtDmXnIlwwkuSpDmm0cSOafRBB1VCIwAAAPIukxxSSFW6dNioiXCCt4UwprGmi2zNNqer6sEqt3UzbcT/+19xaExjZcBmtMxIUUxGpHsqAAAAGqTID9H1ESIyqjTWw+PlkvtEOArd5u/ZhJf9iJwIJ/RrJbOnRkwUFIj6O/HxzpIb0RPh+AGhEQAAAHmXydp/AY8tFBg9ptHZlv91Gt26m4aX3GCdxvDzraq2GX1TEd091R8IjQAAAMhYfc+emkkQ9NoH8cjKXM1EOM7OPDQo/NChMY0R+dAZ3+heacx/l9pccp5v5us0Rt6HWKcRAAAAhS8QsDmvzgUCNmoph1QyG9NYiwYViHD31IBTaYx+MtUZXrd0WWvDS0c4nGJiZBuSvU9qKo0efgFSiLxOzvXJdJ3G2Izo/chIaAQAAPC9frfNVd9b59TrfdoEvzsu/csidZ7watr35+slN6JmTw3eOtks9pmc+8A7GV23dP325U/VZeI/oqqeznWMvJ5VyUJjgjb7yd8WrleXif/Q5q/318wuG8hsncaiqNToj6vVKN8NAAAAQHZt330g5485b+W2tI5rCMs4RNapTHgiHPdK47KNu7LShsffWxN6PKk4PK5S0W1JUeGs6Z7q39fqxSUbJUlf7NgbDvZV1Tat5+y8tpGRMWCZPRUAAAANVOSH6Fyv0+jlgOnkh/CQxhw/lciurzZcSQvtC/1d0sg9IuSrzfmS6ZhGR+QYxuqAJTQCAAAA9SGTYXxeCy2x7S0y+avaBaLCvnPrhMfgbZMEodHtPjZeuCcAACAASURBVPysOuK6ZPKUiyIuX8DalN1avYDQCAAAgIzVV2xwqjANZZ1GKTjmraZ7am7bEjXpTUxYdP5OGBrDXYmz175CEu6emuY6jY6imEqjHxAaAQAAkHcuywImPtZjla7I1hpjQqHR2ZfrSmN8u6pjwmNJcaruqd66/rViIyqw1em9SjVLbtSExiq6pwIAAKChqvd1GjOpNIYO9cqH8biQZZS3SmNk5SscXJ0KYii4JxzT2MDWaXReo8rwmMb03nCRR1UHrA86pxIaAQAAUAAyW6fRW+EltplFpmZjZKCMXUcxK21xmcDICZJON8yUE+F4roNwLZiaEJ3umEYnU0Zmy2qfTJ+a19BojOlgjJlhjPnaGLPLGPO8MeaoNM+1CX6Oz3a7AQAAGrrI4FAfIaI2lUbPiGivUfSYxsjnsvdAddabEj17avA2PLYxPKax2PXcmuOz175C4lwXJ8xnOqaxVAfUKLA/G03Lubyt02iMKZc0T9IBSWMV/Oc0VdIbxpie1tq9adzN45Ieitn2r/psJwAAALLP1xPhxLQ4ckxj5PPee7Aq622JDHyxa0U6lbVEE+E4z8Nzob2WnOdZFbBpfTHizJLqFBaHF32g2//zsFY36iLNPkM6akDwp7xFtpqcNXkLjZIuk3S0pO9Yaz+XJGPMx5I+k/QTSXencR8brbULstdEAAAAuEpzTKO1NulYsNpUrzw3EU78kMYElcbshMbILqluS24E0l2n0SXo+paNqDQ6b84UpcZw99TQgX2LVqmxqnRM1UrpvZXSe/dLp/xcOmNKtlqdNfnsnnqOpAVOYJQka+0Xkt6VdG7eWgUAAIB6k26+yGRGTq9lltjmGuMewPZkKTR+U1kzVtJ1yY1wpTFFaHRuvfYC1ELA1nyRkek6jc51PbFoVfzOo06uh9blXj5DY3dJy1y2L5fULc37uNIYc8AYs88YM88Yc2r9NQ8AAACJ2AS/x6pO89N2ovXsrLV6b/UOfb5tT9Q2Kfih/tNNu9K6/3z6bGtN242RiopMzWQ+EcftO1i3MY3/2rpblS6T6UR2e3Wu83/2HNDWXd8E2xAzIU6qdRpjX6kv9x7Ulq+/qUPLC0+1tXHrWKYa0+jsN4GDGlP8uroUbYo/qMNJ9dfIHMpnaGwh6SuX7V9KOjyN86dLukrSUEmXSzpC0jxjzGmJTjDGXG6MWWSMWbR9+/bMWwwAAICMpOrK6HTpS9Q9dfmmXbrozx/orP97O7zNOXTphp36/n3ztWzj1/XQ0uzYseeAHn33i/Dfp3RpGb1OY8T1qUto3LRzv868523dOnNF3L59ERPsOA/XZ+ocfbWvUlLk7KlOpTHBRDiKrkg6pry8XNf89Z+1bnshCgRsrbrhdjKb9cKeizW18WPxO1sd68nxjFL+l9xweyXSmpjIWnuxtfYZa+18a+10SQMlbVJwMp1E5/zJWtvXWtu3VatWtWsxAAAA0l6nMZDmKhKJujw64/yqXGb93LnvYOi2Mr0HyYP9EUHwtWtP1ai+HVRk3Mc0Vqd7sVw4XVvf+XxH3L6D1TVtcKvoVsd0lS0pTj6mMfYevtpXqV37sz+JTy5VB2xcOE61TmMbu02zSm5UUyWYMbViQH01L+fyGRq/UrDaGOtwuVcgk7LW7pY0U9KJdWwXAAAA6km61ZpElUa37q3hRddDaScyFBWyFuUlod8iZ0+t2e+2TGO64wedoHegKv5aHKxynwgnvC0Q0z21cXREiO1KG/taHawKpN0N2SuC3VNr/k7ndbiw8gWVmCTvxWOG10PL8iOfoXG5guMaY3WT9Gkt7zNiqVQAAABkS7rrNKYbJhKFS7fiW+yRkaGo0ByMTIKhQlWRkZxnEfm8q1yebLqzyjrX+UBl/H1EjnN0u87O4yYa0xjblTY2QFVWBxKOSfUqt+6pKQqN6lGVJMKcOl7qMrQeWpYf+QyNL0nqb4w52tlgjOko6ZTQvowYYw6RNFzSB/XUPgAAANSRTbPHZaLQmKzS6DjoVqIrEAeratrmLMVQZEw4DEc+FbdrkG4Yc477pjK+0hUdGuPPddoS7p7aKHmlMbaZfgqNzmtUFTNjaspnd3CfOtr1cZvX29aa0vr/pCG/qb9G5kE+Q+OfJa2R9KIx5lxjzDmSXpS0XtJDzkHGmApjTJUx5uaIbeONMX82xlxkjDnNGDNWwaU62ki6KafPAgAAoAFKd0xjupXGRIe5jvOLDS1VhRsaIwObiag0OgEtqtJYnTogJ+Kc+43LtYgMru5jGmO6pxYnqjS6t+lAlX9Co42oAMdVGhOdtHuL9MBJKlb0td9jSzWk+l59UerWudJbGuXrga21e40xp0u6R9KTCr4OcyVda63dE3GokVSs6IC7StIPQj+HStqlYGj8sbX2wxw0HwAAAGlIFXpSLRjvOs4v5m+3ZSYKRVRodG4jZk+NVOWyMd3Q6IS2g26hMUX31OqYMY1xlcaYK+52/Wsz02ghqw5Ej2lM+Pyslf7fJdLX8VXGNwLHq6o6oOKitOb5LGh5C42SZK1dJ+n8FMesUUywt9a+LOnl7LUMAAAAyaQbEWJnoEx4XMJKY+KJWxyF3D31QGT31FCp0ZjoipbDPTSm9zhu4yEdlREVTLfDYtcjbBKz5EZsXooNUJXV1jeVRkd1zJjG6oB1nz11y8fSevfRcc9Vn6qADXZH9rp8L7kBAAAAH0u3e2qi0BH5wd0Ji/ET4RRuaIwMbE50KDImosJac2yVS/hNN4y5BU5HVPfUZJXGhGMalfTvg1X+qzQGrI2a8Met67AkadMS181LAp31VqCXJKlRMaERAAAADVDkB+pkyxGkyjxOESbRffx/9s47PI7q7Nv3zGzTqssqttx7x40YTO+dEEoKJYQ3CYEU0hsJyZc31DcJEBKSQAIptCQQ0zsYMBjjhnuvsixbsrol7Uq7O+X7Y3Z2Z2ZnVytLtiRn7uvy5d3RzJl2Zvb8ztPMoskQNU6WroFKTM4c02g+byc322xLbqQVNXSfPdW4xoYVMjV7qs091SF7aibROhiRbe6piqo5xzTWrXfc/qvRb6PFpZZraXRxcXFxcXFxcXHJQG/dU+0uguBs6RqoRJU02VOdLI1OSWp6mD21u2Nwuh/dWRrtVreUOo3HYPZUVdUcJyxSqF2XsugW9XvUMiTx/ViIaXRFo4uLi4uLi4uLS4/JNntqtm6L6RPhaN2uM1gS4SQQzOeS2QUyW/feTDGNZlHtpO0SFty0iXAy3++orGY9OTDQMWJNFdXmnqpqqXUaVQXqNqa0sVkbbfkuuZZGFxcXFxcXFxeXwcobm+poDkWP6D6ytUDtawnzwY4GAN7cVEdjRyRl+4QbpYN7pJ2YorLw45qs3TvNbKltY92+1h5vZ6CoGv/5uAZF1SyCLRYXdqIgEIrIvLjuQLeWxmwP3yKu1fTXp6oxxNKdjZa/q6rG0l2N7GrQCxjYE+H87u0d7G5MFjeob+9i0ZaDlvazFbcGHRGZl9YdSHzf1xxmyY7GDFvo92VtL+6LmfU1rfzh3Z2JfmZH0awC21EUN+4AudOyqFXLZZ9WZlkmHgOWxn7Nnuri4uLi4uLi4tI/HArHuOnxj5k7qohnv3Zyr9rKJBeyNUD96vVtAGz55QV85fGPmTqsgNe+daotEU58f7Y2Iw7uqX98dxf3v70dn0fk0lmV2R1EnAsf+ACAqnsu7tF2Bk+v2setz26gNRwlx5cUYHl+fegtCvDetgbe29bAzy6Zlvh7bxLhmOM6I7Jq2a9ZNP5wYWoMnqJpXPOXZAZQu6XxkSV7CEWVxPcnl1fz5PJqdtx5YcLVNoOh05Fbn93AS+sOMK4sl+mVhTzywW5eWl/L6p+dm3ab3t4XM3e9uoVlu5vJ83v4wkljUv6u2rKn6oLeJv4cXFM3qWO4aMYwnl+bFMSupdHFxcXFxcXFxWVQElF0EbCvpbObNXtHT7NqGhar7Qfb9e8mMWK4YGZjadzfGgYgFJF7tP++oL0rBkDtoa5EIpzVPzuXgFcXcubEKGHT8fVFnUa9Hev16C7m0y74fFKqROiMpl5HsyW1p5bGA62d8Xb1fhiRVdo6Y4dlGT4cjP2muzaKlpoIJ4UDq1MWbdLGMGN4IU/ftCCx7FiwNLqi0cXFxcXFxcXlvxBDKBzueLavYhrtf1YUa81AsxgxPttbdHZP1dfyOgigI40hDrtiSuI47NY7A7MRyuk8srXgmYWiXeBEu8kuaxeZTsfqZM1VVC2RZMceA9hTZFVDVjXH/RwJjEuULutrVjGN+z9O2W6dOh6wPlceVzS6uLi4uLi4uLgMRgyhcKRd53qaVdMuYBRL5k/9f7s4cbIWGcvSibUjSSAeExiR1YSo8ppq9ZktjeaC8U6JcA7P0ti9Jda6rfW7veQGOItGWdVs5TyyOdJ0x6BvHDa5wR5JjOuq2PqbkT1VsbmnpsQ0ylGoTXX1XaeNRxAEi3XRzZ7q4uLi4uLi4uIyaDCLLUNUCYcpGjWTvU/LENXYU+OTveai+WvC0mhr06lOoyHW+sPK4/fqQ+yumJK4zl4xOewW04zAZVVLERhZZ081XQO7+OzWPdW2j4A39QC7YqliTlZUS9s9mSDQbFZjQ3xm407cF5lajWO19x1z9lRrkiLVGtFYvxkUaxKdZgqo0UoRsE4MuHUaXVxcXFxcXFxcBjTp6usZg+UjPZ7tqaUxIlvFiVkgGJ9TSj6kyZ4K6d0PjySG8IvIKjFFxStZLU9mEWE+P1lRUyy/2bp8yhZLo/V6dG9ptO7DJ0kp6ziJRiXF0nj419o4ho4sRGOnw7H0FON62c/duHSqZrc02hpwcE3dLEwEBETBasHvBw/pPucYOAUXFxcXFxcXF5d0mMfxqoOl8XBd5/oqptEuWu1ukIotrsypzUzuqd1Z2Y4EhhCJyLolzh5XabbuxmwlRexWyGxLUCoZYhq7E412y51HErq9L5DqntqTCQLjGhi7kRPuqd2Lxr5IbmT0C/ukgqIlxaS5m8mqar0m+1OT4GwWJwD6uZnXPRYS4bglN1xcXFxcXFxcjmEs1hKzaFSSNQOP1v6zIRKziUaHOo3ZJcJR0/7tSGMcp54Ix0E0mj6bjy+maimWxmyvn9XSaN2mu+QyEds1kkQBjyiklPEQURktHKSAEABa+zSUiMz10ht4UdBqK2DETBA9IKZaK50wDjVpaezeihjqg7jHWCKBj3N/U1TNse8lcLQ0GqLROhlzLJTccEWji4uLi4uLi8sxjJrGImgMmg93PKul+Zxp/9mQ0T1Vc7Y0OglDI2Nof4hGI6ZQT4SjpSTjMRueYiZBF5PV1JjGLC9gJoHjFPNpxm6NFQUBjygSUxQ8yEwRqjmrcy9f9D9FkRBKrvjozwH4pTf+/e9PJP/mDYI/H/IqYMYVcMJXwRtIe9yG0M3GitgXlsZ07suqydJor9NoJMnJpRMatqa0uYm4aMQ6GXMsZE91RaOLi4uLi4uLyzFMWkujfHQsjT2NaeyyWxo1BzFkj2nM4J56tEo4mDGESCSeCMde99B8zc2i1ikRTraGWrMwtAvlmKzHVXYnHg0kUeA8cSXX+l5ivrgt3ggpte0zH1BY/9dxEOrWw/qn4bNPwJDxltWM+Eu5B4lwsol77A6jX9iTBplLvaSr0zhDqCKlExaP4VBHARABQbDEMR4L7qluTKOLi4uLi4uLyzGMNaYx+TmWcE893HazC2rsaZ1Gs6VR0zSLpTGde6pTHcKke+rRT4RjuDyaE+GYEdPENMqqg6Ux65Ibmeo0qokyIN0xVdhL0e8n8YDwm6Rg7AvqN8NTnwXVakmWB5il0bh2qq1Oo2Kq03icuCu1wcq5iX4pYI1bPRbcU13R6OLi4uLi4uJyDJPO0hg7WjGNPTT0mS2DUUW1iKaeuKf2Z0yj2dIYU9TUWpFp3FMVVUu5H30R0xhTVPze7kXjDGE3C32/QOxqyWqfPaZpB+xbYVmkKNYsptnEK/ZNTKOx3zQxjbbsqeZ+OMtJNA6fl5gAEQXBIhRdS6OLi4uLi8sxyKFwjDte3twvWRddumd/aye/fmNrt6UIOiIyv3xps2OpgIFO3aEu/u/1rb2uR7d0ZyP/Wrkv8V0zdWlDnG2ta+etzQd73Ha2MY2KpnHfW9vZ2xTKsFYS8/0KRRRL9lBDDNlv/YHWTu56dQv3vLaVmpYwAJ1xYXHfW9v53aIdvL6xFoCP9zbz2EdVWR3Lb9/enuhnH+5s5GnTtbSzrznM3a9t4a5Xt1B3qEs/lzTZUy0xjeZEOEqqpfHJZdX8btEOHl+2F1lRue35DfzixU1ommbp45liGqOy6lh70UBA5RLxI57x/ZKgEEm7nh1VSo1R7A75jdv45UubExblvrI0vr6xjtc26Pd4dXULv1u0g9tf3sy9b25jV0OHZd0/vbcrcY0Wrt7Pu1vrE39LZk8lxT3VuDOzhN2pBzV8LsaTIAjWyZjDzVA8kHBjGl1cXFxcBiRrqlsYW5pLUdB31Pf9qze28uTyaqYMK+CqeSOO+v4Ph3X7WhlZEqQk9+hfr6PN159czdp9rVw6q5IpQwvSrven93by1w/3UFkU4MunjjuKR9h7vvv0WpbuauKcqeXMG11y2O1c88hyy3erpTH5+cbHVlF1z8WHvZ9MVDeF+N2iHby2oZa3vnt6t+ubLY2dMcVWK8/Z0hiOKvz5fX0gX57v54unjLWIz/ve2g5A1T0X8/TKGt7YXMf1C8Z0eyy/fXsH18wfRXlBgGvj1/IznxjpuO43/rmGdftaLctiskrUIXuqNabRVnLDZmlcuLom8TngEXliWTUA3zx7In9dsoe/friHYYWBtDGNq6qaaeyIkJNiadS4XFzCDZ43mCU6iCAHGrUC3lTmcZd8LR0E+c/NCzjY2s7X/7URgPe/fwajirxQv5mrH3yT66U3uVBaaWnDc2AVW6peZqM6PXHO+v/ZxzR2Olgab35Cz2Zadc/FXPHHpZa/Lfy4hqW3ns2W2jaKgz7+7/VkEhtF1fjb0irOnFIOJPuYoqrWRDiKhiAIlNDGSLHBunNBhGGz8IjL9K9gKZ1yLLinuqLRxcXFxWXAoWkal/9xKdMrC3jlm6ce9f3L/Zh18XC57A8fMq40l3e+f0Z/H8oRx2nA6IRRuqE3Bcf7C0Pw9HVdesXBPfVwybZOoyFmuuTs7ptZ7MVkNauSG2aMOn/pYhlDUdniEtod2d6CTof6gqqmObqnmoVh1GJp1PBY4h81zL6sr2+sY7RQR6XQRKzrJEtcnlNMYzgqc9VDHwEwd1SR5Rju8jzKNZ53uj2vRq2AyyK3s5+ylL/JqkZUS4pRWdPA44PK2Xyk7menWsmF/o0gd1q2+6z0Lh/FRWMyEU737qmCoPe1nvbdznifuvCBDxz/HjYJVbOlUbNZGiVRcI5nLJsKvlyCfilxnK6l0cXFxcXF5QhjDPY2HWjrl/0bM8SDRWsY7nO7G7Nz/xvsaPFhfHez98btO9Ixe0cCe+HzvsIpe+qRwKlMhpDmbDIVkY8pNtFoHH+GZ9Oo85dOWISjSo+S4/Rm0kHV9Osc9FmH3OZzlm3uqV5ULhWX8jnpXWaJu8ghgiRo1GolBKqh2N+sr/z7Oxk6/e+AD0FwjmmMycllYzyNFIgbGCE08i3Ps5QJhzIeewe5XBP5Meu18WnXUVTN0o/s16qBYjjhJvjwt5bln5KWMlHYz7vqbMZuXwMl52TlnioKAkpciPcEoZt3gDkbq6G9VS215IYkCpwvrkptYPgcAIK+uGhEsNZpdEWji4uLi4tL39PfFj5jgDFYLFQ9LWkw2DHOtzstmFxv8A/Y+gqnOo29aM30ybnWHSQtafZ10hExldyI2CyN5gF9OkIRGVXVUpLBmP8eVVQ0Tcuqb5iFV0+RVZWYoqVkTxUEgQARpgtVjAkf5LPSZiYJNcxuq2W2vB7Jl3pvhgnNYFv8P5tu4BTfcFqqPkkk1M7Z3jVMEA9Q9LwPRs6BKZ8jgMZPPE9x3YFFiA7tOlIwgu+qP2J915Buzk+zZK517FJn/hRWPgrRdsvi6eJepot7YTOw+de8DTzpOZvmlgWgzgYxfeKevp7wCJmsxEl3WWuPVTWNIu0QV0rvpzYwfB5AcnJAsL6fjoVEOK5odHFxcXEZcPR3Ahrj932wiMboIHKj7QsSxqZubo9hgR3M47W+7oHmPn0k6xea3WDtdRe7w1xyI2bLnmq4Mma696GoTCxDytZw1LBEavg8qZ3DnmApqhx+IqWYolviEu6pSgyW/o5bD77AOP8m/IIMDYA3vsFhVJKYKO6HPX/Svxg6qxPY/jqF219naw9y1axSJ/GisoBffuMeav70MZDZ20NR1ZTsryl4fLDg67D4nm73f61nETQtgvsegbN+CrOvS7h+aJqWaN+pxEpvCEeS9zjhnqpplgdQVlTmSJvwCbb+IPlhsh4PnJuwNFo9IdyYRhcXFxcXlyNAf4sgw52xt5krjxb9LbKPNobw6c7F0Lh9g9E91cBeeLy3ONVpPFwyxTSav0fi8WTp3FPt25pFZkzRHF1du7M0pusbmqYlrEqOpTBILVcR7YWlEXQR65VE/USfvRE2PcdU6Hvf417QpOVzbuTXNKMnlvqlL2iLrXQmpmiW97XTfVFUDemU78Cm56Axy7qPHXXw4i3w7t1wynegcg6K6KOSRhREfF2FEGoCfx54/Nm1mWl3EQdLo6JZrIWqBpNVh3jGGVdCfgWQtDSKgt09tdeH2O+4otHFxcXFZcDR3yIoIRoHh2bsd5F9tDHuS3duueoxYGnsa1dtswA7km7g5ntjWDTTuafaJ2dSLI2WRDjE20pPKKKkTXQTkdWEVSnd+dv7lX09IyFKtoSjii5ONzyjC6cBxsvKifw8dkNCMBpkc46KqnVraYzKKjm+AFzxZ3j8cuhszv7g2g/Aaz8AdNGy1LCarov/EyQoqAR/Af/wSryvzoT245ko1DBP3M4ooZ6w5me7Nh0llj5zb0RWkRUVjyRa6jSatZ6sqkxWHbLMjjkl8TEnbmmUVdXi+jyYJ64MXNHo4uLi4jLg6G8RJAw299T/Ukuj3E3VeGP8OphjGvv63pq7dG/b1tJ8Buuz012dTPtzZnabjcpW99RE9tTu3FPTvENCETlhaUx3/imWRkV1qKWYPt7OTkdEJo8ueO1HWW8T0yS2aSNZos5knTqO6WIVLVo+7d4SRotNHBdbx6nSxqzbs/OisoAP1Jm8rsynnaDjOp4sRKOsWpPSJOtoWmNac5CgcjZ85T1eWfg32PshU4R9jBdrD/sc9B0pcEivnXm6BKdL6+HeJ3nLbnxUn0F74GGuli7iGeV0ZAcJFIoqFOaIybIuqgaigE8SiSoqI9QDzFfXph5D5ezERyMRTjiqWER3NlbbgY4rGl1cXFxcBhz9LYKM+JNBohn7/XodbbQsLY3GwHUwZy7sc0tjmjqNfY1Zz3d1455qv40W0aioju6p9rhDM0aiGyfCUSUR05huHdm2PCarloyeUUUlkFLzML1nQlRWWdD6ctYWtjcDF3Jv+AK2RZMlLl5TTwBAVGHMkFx+3Xgx/7xqLIdWL2Tvri2cPzSE0H6A5i54ST6B84+fwgmhd1F3L0ZUYwA0Fc7gew0X8p46J6vj8IjOPpVG2QvQYxojDu6pnTGrtThB8WhWlF3FP3Z+AgAfMR6atYezCvaza+WrjGd/Vsd2OAgdtdztfZS7vY+yTy1jpNjAS8qJ/Eb+DHu1oYQiMoU5XktMo6Dpgi+qaLzp+V5qo4EiKJ2c+Gq4p4ajisXDwbU0uri4uLi4HAH6O3uqkelu0Fga/8vcUw2x2J3oSWRPPeJHdOTo63vrlNX0cMkU0+iUCCete6rd0hizJ8IxtZtFncZQJH1JjZZwtNv+42RpDEUVRFTKaYEdb0O0Hpp2QKAQPvFlyCm21Pozk0+Ys+v+krL8gFbCLdFbOD9vB41hjUXqXBr8o5lclM+BznacsuKoGuT648LEN4RVZVfyyLY9MHMKa6pbWb6niZZIjPGVMzjhhG+wcscBbnj0IzoJ8OAVc3jvqTVpr5uddNYxryQmJqpkRbNklzWubciUWCbTpFYUL5srLuGssybyyeXnoEVD3OH9K5d7liJoR+69NlJsAOBSaRmXSsvYp5YRfOtCOO7iuMjW3VRFQcAjCkwSahAFh/4y6QKQknIqN2FplN06jS4uLi4uLkea/racGT/vgyWmsTclAQYjhsjoPqZR/z9d6YWBjGFJ6+tn4ajVaTSLRjmze2pKIhybe6oajyFU1GT2zIyJcKIysViMucJ2LpRWcK20iKAQYbU6gcIlZ3CvdyvDhSbKXnwEKqfC+LOgaCTkVUBOEYqiMk2o4hRxA5VCE+NWvEKh0sAG/2JyhQgstO1wzRPw9RVpi9J/2fMKPrUrZfnXot9mrTaB/cIs6hT977lxoZJppsNwgTTfP1nVkFUVv0cCYonrFNG8dKIHAub5ezbsTyd0fGbRaHNPVROiMSl4u5sENMR7TNXAE+S7sa9x9g/+TWHzOtj7IVQtgeY9qF2tdHTJRPDgl0QK6AD1MNLNOjBSbIBNj8Gmx3hZGsoPlRtRtVNQNQ2fR2RGdI/zhsd9xvLViGkMRRSraHQtjS4uLi4uLn1Pf1vOBludxt6UBBiMGHelu5hGw7I1mOtY9rULaabsqT1N8KJlqtNo2lGPYxrtlkZVr3NoFo3mTYKSynxtPQvETcwWdzFUaWH436M862+xtDtX3AlbdzLa8Cyt3gLV78CyPyRX8gSokLt41RwTZyTMTHdpWqrQNj1HOJLHDGEPc8SdFBJirFjHfm0I3/Q8n7LJXv8k1naNT5xj4nzj9yCTO6Mh/qKKmrjqUVlFVjUCXt2l1Mi6a247P9CzYX+6mEZzzUlZ1Szi1bAwm7OR2icn7D3aXBexJNdHQ3uEDlWicMzJVWpWFwAAIABJREFUMOZkOP2HANS1dnLSPe8AcNaUcv76heP1eMauQ0S7Onj+kbs5Q1pHmTdCKKbRqBWiIjBCaEgtk5GBMUIdT/tv5+H2b7G04CI8oqjXk3Ri/FmWr4YVuNMW03gs1Gk8BhLAuri4uKSnvq2Ls+99j+qmcK/b+t7T63h0SZrZRhdHnl1dwxf/vrLH22WygKiqxqcfWsqiLQcty7/495U8u7qm27ZbQlHOvvc9dhxsT7uO8fseU1R++dJmfvNGlmni+4kjWW+vNzy9ah83P/5xyvLv/Hstj31UddjtGoLkhr+t5Nz7FvO3D52fS2O9//fiJj754BJe21DLpx9amrRWqRo3P/4xy3Y3HfaxHCmMiYtMVprXNtRy3SPLe9RuVWOITz+0lH3N4ZTn7IzfvMu59y1mf2tnynZff2o1/1xRnbZd47l8b1u9/t1ccsOhf/5rRTWn/uod3tl6MEU0mi2NP1q4gXU1rXrJCpKiRAMKCHGX5xE2e6/j775fcZPnFU4QtzJaOIgnYhWMWSOnWgSzQXjuJlZ5b+Rl/23c7v073/c+w5XSB46CEeBfpd/AUKHmSbKorMZFY/p9GcIkpiStfIa41i2NSSFmvsc+KfvkPZDe0ugx1Y9QbEmCDKv+bc8nk/Sce//7tHfFUtYxfzdqMBbm6AUr39tWzycfXEJEVvjOv9fy+EdVlsmfmKLqwZVFo2DoTCJDj+eH8k3Mj/yR8A9qmBH5K2dE7+es6H3MjDzKQ/KlHPSO6NH533ToAf6x73z+Efs+X/K8lrrClY+CTdwbxy+K1qzNx4Kl0RWNLi4uxzQvrjvAroYQf1vae7G3cHUNt7+8uQ+O6thm2e4mnl65jyeX7+W7T6/jna31PW4jk2gMRWVWVrXwpX+sYunOxsTyd7bW892n13Xb9rvb6tnVEOIP7+5Mu44xNonKKn/9cA8PZlh3IHAkE5r0hh/+Zz2vb6pLWf7cmv38/IVNAKzY08zi7Q09atcsMnbUd/C/Lzk/l+ax6fqaQ3z1ydWsrGqho0u3grR3yby+qY6Ve3pQAuAokY176lefXM2SnY0piVsy8ZXHP2ZlVQvL9zRbkpUA7GvuZEd9B/9cnioOX1lfy63PbrAdY/JzazjGyqoWvvUvPbukU0yjmaW7mtjX3MnyPc2piXBsx1V7qAtfXKis2NPM4o17+VLTb1gfuJFrPO90f9JHiUIhy8nJipn8eU8yyY19YkASkzX+FowbwrfPmcjskUWJv+f6pcR2ZtHYGVXIi1sTDWGWzmvjtoun8sWTx/KjC6bw/y6dllg+f2wJ91wxE7CKQzM+03JZtdZpVBSNP7+/i/U1rZZtVpieMXufVtRkWZWC+PH/9LmNrK85xN6mMM+t2c/PXtjEi+sOJLaxT0SY34H26xnBxz3y1ZzQ/iumdz3KzdFvs0KdTLZM1hzKbABUpiYUOn/6UL5zziR+fOFUS9Zmyc2e6uLi4jKwMWan+zuxyn8Tn/vzspRlUdm5iHY6MomgsClu6JpHllN1z8UZMykeDoqaHIgNBvo7BrQ7VFVL6571mYc/AqDqnouzby/L253OvTgUlSkMeukwFXkfqGTjqh2KKBQGU58ve/3D1O2yiwfL5vkK24Seed92EQjJa24IeDOp/VnjVGEt0zxrmLl+Dws29e/kXbOWR4nQcfgNnPQNftE5nfe21rNqb0vKfRBIWppvu2Qq0ysLOWHsEK7+i/5uzY1n6IzKKlE5ObnQEZEZVqjHLxoTCeZrOb48N/H5y6eOS3x+e3PSa+Ouy2cyoTwPODz31MXbG3h8me7KOWN4ARv3t6Vsb3/e9HhM/TwMS50TvzZ5fNjbiNmstekIkcPr6nxej87nza/N5f1/38vQtvWU0M5JUvb9qlotY1TJuJTlkijwrXMmpi4/BiyNrmh0cXE5pjGESm8ThfS1KPlvIxyV8Xl8Wa+fKUbPaaB7ODFrmWr3JRIzDFALnp2BLhrDMSURh9UX8YXZxpqmW83oQ0a2y+gAvs/ZCNqOuAhO2TZDzGc4KlsyXJqxxyemc382X1/7c9ldnUbjvNodRKOxvojKCeIWfuf9PWVyW49HrVFNIloxi7z61WxSR7NZHU1+fj772jVatTwCQpSJwn4qhBam53Xg62pMJFaJahL7tVLeU2dTrZWTn1/AWaedzqdf7CKGxGLfdxgt9tCLwpcHF/4KZn2OzwOfP3E0x9/xVoq7ZldMTbg2GrGN5km3pHuqmphUiCqaPhkSF112S+Pyn5ydKAdhx1JP0PQ5nXuq1+yeGk+E45UEYopm6QfFQed3fopoVNKLxo40Exv2NszvwGxj4r3BAl4IfIoNTWcCMEveyVO+O/VkR92wSJ3L//RACLrZU11cXFwGOMaPW28TqwzUmLHBQkdEpijNAMKJjDPFDgPdw7m/mSYCEpkHB8l9H8iWMtAFhSEajcLqvSHbOZx0iXKMLJfGgHQgim7ZISYtHelKPWTatsNU5L470g3czaSKxuRnp+fIWGbEuvmIsUDczHHCLkqiCuN9OzlR2NyjBCYr1UksVafzujKfS+dP5rfL23n2sjO57fmNrN2nu0uOIIcaOTVm88krTuDkccUgR9i0v5XLH15BlKSAmZVXyMlDpxJDt/b9Sv4cD/j+gAebhVXwsMUzlZouP0OEduq0Iuq1YmbNOp55F1wP+RWW9Z1qIXbGkpk3E6JRMovGePZURSUmJ70iwhHdPdXINAvJPuBL42oK1iQt5jIb6SyNZgEkKxoxRSPgkYgpssUt2fzOt9TelFMtjYriLBob21MFnEcUUtow/wZkO9nnlQTL5MY6bQLnRH7DFzxvcrPnpbTbHdKC/En+JP+T1V503DqNLi4uLgMcw42mt6Ixm0GTS3rSWTTSkcny43Qv+rrkhDFgH+hizGAgih4zHREZY6icrUtkJrK1NMpp+pFxDEa/HIj3OWoSA92R3hrjfP6SKBCKyGnvhWBLE5puPbNFssP2jJstyobl0HzbYoqKjxinNC/kt/7HrPGAGj3OunF+5B62aaMS349TyojSleIWX9/mbEXqiMggSuALEpEiFsEIetF2r6mtV9QToWgKXxzbyj+WHyCGxCFy+eENn+ZXi+tZuquJfL+H9rgwv3/CLObZBCM4W6C6LKJRX+b1JNcLmtxTzTGNHRGZXJ8uGu3vsEzhAWbXSbOITRfTaPbSUFSVqKzi90q0R2Q6TeEDxSbrtyWbqq1fKqqamOCxi8Z6B9GY45VS+rYlA22Wz7NPElNc3WsZwj3y1awqvYwrmx5mrrea/Z6RrAsPwU+Meor4j3Ia9RRntQ8D19Lo4uLiMsBJpB3v5aA63EPR42Klp9alTCIobGtL07TDmhTI5J462GIaIwP8OM3PT7oJBN3FLTulkK2La7p+YQxgQwM4pjEpBro/10zX1Ilcn0QoomS9XTaTPnZhabbkx+QY84VtTIm2w6oqqF3PnfUfMD6wE9rJWJMw4z41Pw/Kl+M743tsW7TD8rfWziige5uYH/V0fcL8XnHqX0GflGKtq/WNoXniBF78aFVimT+/FK+kJ+jK8elCCtJnLvU4JEjpiikY2k1wsDQGvBIeUbC4p3ZGFSKySq7fg0cUEu8w412a6dkyGzvTuaqmw0iEk+PTG2ntTGZJNVsazdZw+++xbCqlUmATjQ1OotEnpVoaTd+7K/Fi4POIaT1O1KIxfLXuO0wZkk9hjpflbb1LlpXlq21A44pGFxeXY5pkvIdraexPempd6s6tzkxnTOlz91RjsmGgW/AMLEW+FTWthaC/MN8zc18wD85DPXBhztY9Nd39S1oaB657qtGns3GRTjcpk+68cv0e2rvklOypBvZnLF37mWIaxUN7ucvzCHPEHUwV9+kLFeBl/eN4xxYz06V5+aN8Ga+oJ5BbXMHGZhEVkV84xHO2hHXx4pWErPqL2VLqZKEO+j2O1rpcn1UM5vk9CYGWY/qbN032TCcLVGdMSZTOSFgaTc+0VxLwSiIxJZmExhBruX7d0hizvcPS7R/slsbuYxrN7045HtOY49WPt80kGs2WxpDJAml/X8uKRiz+LsjzexCFpHtzQ4ezaDQnRAPrREe2ni1eSUw7AVWWpxfqbO+SKc3zO66TDQGvGI9RdS2NLi4uLgMas+tOb+iLOKz/ZnoqGs33y15w3D4g6OiSe2RJzsZKNdjcU62uWRqenpVjO+KE0ohG83PVk7hXJ/dUp8L06e6fMYBNiMYBeJ8Nl+ts+mC65yvdeeX6PdS3p69HaB+QZ5o0K6Kd2eIuRkabmejZy2itGR6+g/G16xjfB6PMmCaxWptIe9447mg5myptGADFXV5UdIFSnJvab1rDuqUxUyyfmXSTGQZBr+RorTOS0pi/++KupIaQAiyurWacrHldMdWyLVjdS32SiFcSLO6pxvnm+qS4pdFIhKPhk8SMnhVma6eURUyj+frIceFqCORDJtFYYrov5j5k79OKKabRI4nk+jwJC62jpdErWfYDJLLIQqo3Sjq8kpjW1b0sXxeKbV2xjIK7OwJeia6Y6hi7OthwRaOLi8sxTSIuqJcxb30Rh/XfTI9jGm1JE8wz9vYBQXtE7ra0gBlDEGZ2T00OuAYDma5Xf2GxIkbNQjHZFw6FkwO/nvQRp4FeOCqTH7BanNKJpoSlMTpwYxp7MuGV7v2U1j3V73EcjBvYRaLZvdhcPqdk09/50O+QbbK220NOy1JlGiu1KUR9RazqHM5qbSIxPJxeWkZVc7KepznrqtNkQ2vC0ph0T83ze+iIyAnrj/UcTaLGIYFS0C85WxpTRGNSXAZNz6E/jXh1EhO6e6p+TsZjZBa/Po+IzyMRNbmnGpbVXL8HjyRaEil1V+7IbAXzZhHTaH7+FFUXrsVe/XgPZeGempoIJxnT6JUE3RKeQTQGfVLKRKH5WQ9Fs7U0CqR7wxuisSMi9yoeMeCRgBjHgGZ0RaOLi8uxTcLFq7eWRjemsVf0OKZRSS+C7APaji7rj3pETrp2OWEMpDO6p8YHMF1ZDj76G/MgLKIoQPpaZ0cLi1C0xDQml5uTXHRErJaDTDjNEYQiSopoTDdZNCjcU+XUOntmzP033SA5rXuqT2Jfc/pC9HYRav6eKJ/TWs3QZbcjCtnfNyfqtSJ2qpWs18bzoHwZHQQBGBoMUKclraF2a4+5TEWRLQ5OEoWkaPSICfdUQzQW5fioi1ktrWb3VMXJPdUnOVqcjEymBroV0ME9NZ2l0aHNiJx0ZzTus3l7ryTikwRispro4wlLo1+yxjQqSreiUUrjkmq2NApC0h3Z/Pzp7qkagbhl1GylNt8X8/W1T+YopphGSRQImq5p2phGe51GU19Pl03YjiAIaSccDdGoaZknGLvD6ANuIhwXFxeXAU7MVPi4N7iWxt7R05jQTDW37PeiIyJbZvRDkcyiMZu+YAxgBkssq909dSAQisgEiDBeOEDRgVbYmANAyd4mrpU2EyBC8OMNfF3aRb4QpnzZu7A9F4pHw9BZECjQR2yBAggU6p8lH0gehtHERLEGAD8xYkhEDo4E30jwBsGjD/iiioqAyhDaGSE0kCNEKOMQY2q3w5Yd+A5JTBGqKYzI0NWmbysNjKFRtBtLozke8XAsjc2haNp9Oz1j5s9FQR/sfBtRzV4wxjSJtdp45s2ajVg8ml982MUH4VHs0oY7rm8fZGeKCSuxuafm+iTa4pZIs9DLC3igDYqCXurarKLRfM72uomgZyx1cnW1WxoFQUiKRrN7ahqrXToxYSw2DsV8Hl5JxOsRLZZG47lPZE9NJIHTunWvFLOIafRJYiK+1mxplBWNiKyS4009P/N9CWVwT5XVZJ1GjygkyvNAevfUmKKhaVpC0B2OpVE/F+flhmiE5L04HAwxnS6T82BiYLwZXVxc+pxwVCbgkSz1l+zIiko4phD0SiluKLKiIqvJ2cO+PC5Z1Ws6qZoeQJ/n9xCOKokfX6NYsHnfajxDmyQKeEQh4bqS5/Mkalrl+CQ0TUv8zcguB9ASiqJpGuGoQo5XoiNuBck3ucEAieMyfnQVTSPP76ElnBxgxRSVzpiCJAiJYzZmg40fsIisJH5gBSA/4EVVNbpkJfHjmO/3JNZXVc1yr8znYeynK6YgickBidGeuWBzZ1SfVe7trKbT/VdUvXi00V/M52iQbq/NHcnrp6p6on7DEuWTRAJeiVBc/IWiimWA0dYVoyzfT1c84U1r2DpQbWiPMKwwkPje3hWjMMebaN8ripYZf2NwoWh626IgEPRKCII+Sx70JdO5m11h27pi5MctFcbPv70ftzsM3o37b7TnNCA1t2UcnygI5JqyL3pFEY8kJMSCP249EBBo6zK7ecqW7/l+D6FIjGB7NV01qxE76kGNYdwtn0dEDjUjRNqQBJAjneDxI3q8SJ0tEGlDEb1IXc0QDaMiIAgigiZDKF4MXVXQVAVBU0FTQFOpUBU2+6OIggbr0P8BpwGnGQaI9TDV+LzF8bJY0AQRRC8fBRxcK5/6dfyCi5BTgppXzrMdNRQH2lLX3aP/uwW4xQ/UAfcAngAUjYLisci5FUj+PGKBYlTRA6IXTfTi9wcQ88qgZByhwFCCeQUIopR4v+T6PURkBQEhYeEx/83+HJvvm6JpCOgWMePZMtz9umIKHlFIvKs7uqxCLqaoKKqGICST59hjEw3y/JmHf+1d1j7UEo5STBsVQivyx48T6dyJ/+M/Z2zDYJM6mvvlq1imTqWDICvOPZscn8SLH75Hs5ZeuNrfYZneaUXB1PIYCdEoJt1T8wMey/9mWjujevya6JwcJehL457qS23LFxdpOaa/pYutTBc3aAg541hS3FMlkXBUIWJLZmRkT+2MKbR1xQhF5R5ZGsU0lkafJykazQKoM6YQlRXHsYL5vhzqjBGVVf132eYB0NYZS7QtiaJlEtDJxdy4rjFFQxQ0wjHFkoAnU7yunXQxjWYraW+S2ATiYjrbjK4DGVc0DjAu/f0SorLKG985LbEsIitMvu11br1wCjedfjj5xo48v1u0g/ve2s5njh/B06tqqLrn4v4+pP9abnxsFW9tPgjAJccN48Fr5gKweHsDX/jrCt77/hl88e8rKc71IQCr9rYwb3QxC796kqWdCx/4gB31HSz+wRnk+T3Mu+Nt7vvMLK6YOwJI9sufXjSVG08bx+TbXuO86UP5/dVzEm2cfM87jCoJ8s+vnAjAkh2NfP6vyxMuLh5TLSmABz43m8tmD+eKPy1lXbwQ802njWPOqCJufmI1AGNLc7l45jAefHen5XjNxYwNBAGmDi0AoK6ti7G3vtqja2kc3y8uncbdr21NLLvwgQ/YWd+RWO/40cWEowrDi3P4y/XHE47KLLj7nZRAfTs3nT6OWy+cys9f2MirG2opCHgpCnp59msn88CiHfz27WQK+ds/NYOfPb+R4UU5fPDDMxn3k+7PZd3Pz2NfS5hLfr9E399p47j1oqmJ+/L98ydz5Z+WAvCtsycS9EmJ8zT48MdnMbxItxBd+8gylu1uZtbIIv5y/TzOuXdxYmDWHY8s2cOn5gzH7xE59/73LX8LeEV+evE0fvb8Rsdtz753MXd8aga3v7zZMZPkt/+9lpnDCxPfb/nnGnJ9Hj7a3QTofeNfXzmRT4wpAZLW55fWHeCldQcQURkhNFBICAENAY0JwgFukg5RFo5S7j1ACW2sv/NOvIJCER3kEEEAGpFooAhVE2kmDx8yEbw0aXq/UxHpIIdWLRcFCS8yXmR8gv5/kAiThX0UCiHLOcXwsE8rY5s6kgqhhaFCC3rxOj0GR0GkVcujRisjX+jkAmEv1/p0cRT7g0ZNYk1dhFQKjYhCZ9zxLxVfms8G5sFCumkk+7BKcFrYSwRNBSV9LB4AmgrhRsRwYw8rqQFyFzRuh8btiXPOlJonF5AFL56yiWyNVfBBQ5C5Uybw2FaNqLeAYZOO52/rwlx34iieWFbN7ZdN5+cvbqIsz8+Kn57D6xtrufmJ1fz0oqnc+aqzal5Z1cITy/Zy2/MbmVCex8u3nMKUn71uWSccUTjpnncyximaKTCJpvFluRxsaOAUcSMniZuYIe6hpK0d5e4QBej98jvA9wLx9+uS9O3Wa0XcGbuGDnLYr5WxRxtKxHYF59+1KPG5JNeX1uJpTwSTyepvF8FmS6IoCowrzWNNdSuVRTmsqW51tMa/sekgb2x6E0kUuGxWZcrfc33JrKj+uIAaV5bnKGYN4RUwibV0ccZOYqss38+E8jy21rUnBJTZRdIn6RNhxm+9mcIcLwGvxGsb63htYx0AkyvyHfdt4E8jKs2id1JFPh/vbSHP77GI7hfXHUjs1475vny0u4lJt73muJ/V1a186g8fAvrvgZMQN2MIurpDXXzhbyvY02h9fz68eHfG7c1MLM+jpqUzJc7VXPoj21JATkwZms+a6tY+n4DvD1zROMDYsP9QyjIj2PuP7+0asKLxgXh9pKdX1fTzkbiYf0TW1yT703Or9Xvz8d4WdjeGoDGUSIe9q6EDOzviosh4mQI89tHehGg0BNFDi3dx42njiMgqL607YBGN+1s72d/amfi+p7EDTYNTJ5bywY7GFIvLrgb9xW8IRoCH39/NZ48faWojxPaD7ZTn+ykO+th2sB2wJt2oLAxw9fxR3PvWdnbUtztep2nDCjjY1kVTKEpZvp+bThvHx3tbEj+ykHRRWrqrKbFM1TR2NXQkzgF04Q2wubaNrz+5mnOmlXOoM8YnZ1Vy3IhCHnx3Z8I6Jgjw04um8uiSPeyqDyWuK0CjyRq3qyHEkFwfXzltHHe/tpUPtjckrmlHlvGBNa1hFm9PJo54+P3d3HrR1MR9eXNT8lwfWLTDcfb9i39bya8/fRzHjShiZ/x4d9d38OiSPYSiCj+6YIrF9emOV/SB70njh1DT0kl1c5iyfD8N7RGu+ONSyw/x3FFFTBlWwFPLq1myI3mcAa/I98+bzPiyPKqaQvzvS5v5YEcDEVnl2hNGMbY0l7te3YKqwZxRRdS2drG1LmlNWl9zCD9Rvj1dY1aglo1bNrPukccZMW0Iw4pyOHVvM5O9ewnSxVh/G0PkgwRJMzOtkV4lxRlHXeYVDpPZ7OJSadkRadul7/BoMajfzFQ2M9UD7ITjPeh9Zxvc7C+ibvM4TvcKeD8azv+IhdSESok1TeBfK/T39csb9Mwxt108ld+/s5NDnTGKaOdPZ6g89f4m9m9oZLQgQ4NMa9tsy/6HFQZoCUctgvHSWZUc6ozxvun5NxAE+Mrp45mUH2Fm/YtM61qD1LEEQeu9JWT9BQt54QX9nH560VTyAh5K8/zsbQoxsSKfqsYQMUXlT+/toikU5ZLjhjGxPA8NGFUSZH9rJz99Tp88uv1TMygKetla1843/7mGtdWt/P1/PkFDe4QHFu2gpqWT7583iU+MKUEQ9IkhSRQoCHhp74px1UMfJY7rjk/N4JLjhjG0MMAr62vZXNvGMzcv4NPxdR743OzE9Xto8W6eXbOfieV5/OSiqTyxbC+LttYTU/U6ok/deAJThxawZl8LC8aVAvDMzQvQtGTiG2NS8ZxpFVw0cxjNoShjhjhP2dx64VROn9TIyJIgmqaXqRhTmkue38NV80YwpjQ3sW4wXmpCFOGXl81gVVUzgiBw+8ubAfj+eZMYUZzDnZfPYE118nd0zqiixOcXv3EyxbbEQWNLc7n307MoybMuv2reCKYMzScUkTllYinPrznApIo8Al6JNdUtjCgJsqu+A1EQuGjmMBaMG8L+1k6mDStAFIXEfVE1jTXVrfz6jW2Jtm+/bDqKqjGuLI/r/7oisb/5Y0sYVhjg1Iml/OKlzYn74/fosaKt4RhzRhXx5PK9XPnQUhraI9x0+jjK8vxEZNWyj+tOHMVls3X35zXVLdz1qj4pes8VMxk9RL+uD1w9h3X7Wpk8NJ/NB9q44W8rAagoCPC7q+dQ39bFWVPKuX7BaDT0yQyfR8za+vj/Lp3O2VMqmGGa2BysuKJxENA5SBIxmLG72rkMTIzkFPb4FXNguJ5pLnWuvSva8xhBIxD+nKkVCcFlPR5nMTQk/kM2ZWg+W+vaaeiIMLw4h1ElwYRoNDNqSJAvnzqOe9/aTkzRKM3zEfBK1LQkBez0ygIEAZpCUSqLcvjyqeMoCtZYRKOBkaxj9sgi1sYF7SkTSlmxpznF8vX6hhoWbahilNDKNRUxThSrGS29g9/bSJHQwfjyfHI3yFwVO0DHvgL49yweLNToDLVRJhwiT+iEhc9xfp2XK6QDnLGhlk/5DxLeW8yXfH78xPD866/cGo/ZkpFo0IqImhKfdGgBGijC2zKMnGjvklRsO9jOdY8sZ/0vzjdlnJTZuP8QM4cX8tUzrBNZd7+2FUXVOG9aBS+tr6W6OcyFM4Yyd1Qx3/73WhpNNbfmjirm/BlDeWp5tSUhSnHQx5dPHQeAqqj89qUVhA9J+Ihx3cgmpmoryB+yhmhrLacLGrK6lxZJIChFqPC0E1DCBIgi7tL78Zmg/9ptj+8XkkJwcIQsugxiKoRWKqKr9T7X9jFnGI/q7+/nQTGftd5RtLSOJOST+dzOLj7DOvz+TvyCDMtggQ/YDz+Kh1hpf/gJL/tGsE0bwdDSIbR2yoTq8hkrCagIBIhyZcxHSIlxtqeVKF6KhQ5OFDdTrxXhF2SG/7mda8NNaY74MKmcwzknzuN29uKVRD43f1TKKqdPKgNg4er9NIWiFOV4+fyCMZZ1mjqizB5ZxPyxumfApIp89rd0MnloHmdMLgdgWmUBTyyr5iunjU+4Xp44boilnae+fALL9+gF2XN8EmdO0bf98YVTmFCWxyfGlPCna+dy4FBXQlgAjCjO4Z8r9nHT6eM4aXwpc0cXc/erWzh3agUAJ43XheJZUyoS2xheDAbfO28yU4cVcN60im6TqEyrLGBaZYHj34zzNXgBPm9BAAAgAElEQVTj26fxh3d3MqE8D79HYvZIXQyOK81lS10bXztjAgDzRpcwb3RJSnsAx40oSlkmCAJXzhuRsrwwx8vJE0oT3685IXlPDRF0pukYL5w5LKUN476cNL4UnySyta6d0nwf1504OnFtFn71JF5cu5+fXzodSRSYUJ7PhPJ8cnwSoiBY7o/BDy+Ywoc7G7nkuGH8+IIpCILubXSgtZMr5g7nmVU1fOecSQyJ11icNaKIPY0hbjptvEWIFwS8nDpR75flkwM8dN08alr0JFGfNFmcx5WlHEJWBLwS50yr6H7FQYArGgcBgyURg5mQQ+pzl6NPdwk/ovGCvJ0xxZJx0pr1UHa0QGWquZSOUERGFKzB8cb+jb/LDm2FIjKFOV6uXzCGnzy3gfq2CGNLcxOxfOY2QHcjCnjFRIFgr5Tq7pLr9ySWGcWZ7UWajbYb2iN4kJkj7aJIrGa40MQpVW8yyruTIqGZPCGMF4UhQhsltCEJcdEd98I8F5IiJa6Vi4AitRW2VHMJWN/GG7bry+LrVwiA0spYw0Omahc3ZfP2fuZOJgGf8/tpJp9GrQAWPs+PPZ0c1IoYc+h4jheaOEgx9Vox6X4SNE235HbGlMS1bmiPMLQwJ2Vdr6T/cPtEhUpPGzsIMSKyi0+VDuNf/ir2RXIJ40dEY1LXOkbseJsvSduItZQzXWqhXGhhjlIHf7kLWvYgRtpZF4hCI6h+AfFl/dp+1rhmhmHduDYqfe4S6dJLCkZA4QjIK0O/ORpIfsgr111JBZHffNhCTIWg0MVlFU2MiOygpT1MiCBlNOMRVCRRxKNa3S8btEJqtDKmFIN8qBZNUykQOh0PIzZkMt6cAsirYHdLjObaPYwWG0GTySVC0F424iiQp7ZzirQJYpv0PrwXCiBjHxZUmRliFTOoghbTH8w/uXHvvOm2R3qEEH8BpU+ceniUTYVL7gdBSBGBThjeK06xdt88e2LKMvvk1PTKQu6+YmbGfZw0oZSTTILH4GaTx5aTyLlgxjAumJFcXpjj5Z4rj8u4LzuzRxYlBF1fMrIk6HgsZ04pT4jigcyNp41zXD5vdDHzRqc6k3/2E6kTDwY3nz7eci9BD0W48/KZ8TatotnnEbn7iu7v4wUzhna7zn8rrmgcBGRbpHQgEY6mpj53Ofo4lTlQbEHfFQV+qprChE0ZJ83JE0JRxVKjy6ltp787pbEORWVyfR49g51t/4l9OQSLd0QU8vyeRGrz+vYuZgwvIC/+vbzAz96m5CgoN55gxigQ7JXElLTouX4psazQq8L+j5lQ/QE/9HxMhdCMD5kCwkzy1OLpjJLr7yJYF0kGOO2B6dCt6+JAIChECBLRB4wbdnOzcfm3Pc7VceuFrIk0UkyzL48WLS+R6EVDQBV8KK+9w3c9dfgCBdSqAgcOjWZu0A8r1kHTToiGoO0AL0vbKBFbKX4jxDVoEAA26//+JaB/N4iHMP7MC8RIDnplYH/qeYhCap9y6QG+fBg6E3JLIacY/KY4J9HDr94/iFeNctqEEuYN07OUklMCOUUQKNIzkuaWA5ou9kBvy5MDogSCpPs+Gp9FCUQPSN3/Fjy54s1EnTl58gTOmzaUSx9cwpghQaqawowvy+Urp43j9oXLCRBj+W3nMf6OZRjq6tkrT+J/X9rMun2tBIhQLrSSRyd//sJ8Lvx7Fe0EWXnDOYmMiKtW7eOH/1mPVxJM8W0aFbRQIbQwSazBT4whtFEaULl+fiUoMbbsb2Lj3nouH6fSfmAbwViLbhH8b0DyQf5QGDIBKudA8Vi9T5RNhooZ+v3OEiNGrDexYi4uLkcXVzQOIMw1l8zunR2DoD6cfVK0IyJzbBjjBzehiJxISW24gbTZkrOU5weoagrTEZEpjlsAzVbEUEROCESzl40lBbuDOO10EH+hiEyu32MJjjf2n9iXg2XdyKppbBdTNN1S6DcshalWRDSNkb4OvNEazlRrGdkV4hJPPaOEgxQLHUxYE0aRZWL+CKVV7fAXhYnARPtbUc89cszjEVSG0sRQMY3L2srVfNODVdzVklLEewIMyusVE/00KUFatDxieCiigxA5bFDHUq2VU61VcIhcCggxYfwEvnX56Wyt6+AHjy8mT+hkekWQloP7mD+6kM/OHw2helD0Z+2F1dW0NdUiIyF6fHzhlInx0hHe+EB8GDvVCr717w0APH7DHEpidTz+xhIiLQeYMrqSUxacog/QNQ0i7dQ3N/Pwos0MFZr5zPyxFJZWcshXwVcW7qWDAPd/djaTKgriD60AvlwoGk2mCtN/ee9VYopG2dQZzDtx9NG47AnMyUQ6InLi/WK8H4I+/XnvIEgHIOWVYu5oPklMTCJ14ada03+BfMNn0k69/tlk1TK/S5IIHKSEg1oJ65WkBWO4N4frzz8LgHUrqvnxrg2ccuVZ3PbcRhZtrWd8sJNFX6jkzsdewNvVQoXQzDChmYlCDWPF1EQlA5bcchhzCow9FUadBN4cyB+WFIOCaP0R6AVG4hVXNLq4DB5c0TiAiMgqo4SDxDQP4ahMXtxSNxjqw9nn/wfDMf83oGrQFVMTpSjAWkwboKxAn3m3u6SaPxsDOLORMpymWLfTMkO4hiIKQb9kSadtroXUkU40RnWxaS4tUS60M7ljI5+W1nFKuJYCbzUlQjsFhCjY5oP/a+PV2CHwA13xf+Y3npH3ZBAKHJcsKRiuW0FKJ7FwS4gWLY8vnziMdz7exLKDInu1Cr504cnMnzmVR9dGuOf1bd23CXyuaCSUjMMjd7BB2wcalJQN45XaWvKGjoY5Myzrv31gDS8djGcY9Hr5wjnnpbQpNnSwSdPjc/1j5oPfw2sfVbK0oYnvjZvEKTOsLnvKoU4effMdAC4/6xzI8+ONyiz/zxt6e5WzoTyvZ9crjt0qf7QxTx4Z7ydRcC5tYODziOR4U/+em6bkQTBNJsvuCMbFpvm9uD+WC6NP4qlYGyHFOllWQIiRQgOVQiN+YkwUayijlaniPo7z7kPqLhNs4Sg+ClUwLFpNrtCFn2ha99seI3qhZCwUVMKMq2D2tRknFfoSQyx2VwrCxcVl4OCKxoGCpiE8/1Xe9/8bgM4X3ocr/wge36AUYKFBYB09FtEc6g2FojI5PolY3F203lbQuDwu2kIWEWj97OieGnEWmcn9JreJyHrNxVBUJs9maTQPUMNRmVBEIY8wo4R6KoQWAkSZ0uphvFbNzEUHeMO3lwIhzLBNeoKDC71ABKub6NEPTbIQ0by0k0NR2XA8Q6fzavNQXqjyctrcGVy7YCwAf/m4jXeXreSxq0bwm4WL6cRPg1ZIO0H+ckEuf357A1NGDeW8s87hltdb2L9vD0OENjrI4bJREepqdpNLF134GIK5Dp1GudBKudDKxMAhcqLN/XMR+pAYXjRNxScouuVj6HF8uK+TLR25nHryaaxry+fFdfuJaF6uPft4fv32HtRAIR999/JEG282rqKqMcyXTz6N/1R9zKsH9IRHN1TOg6Ih5Ab2Zn08hoXb3I9L41b6oEP9O6ei13bMbRmlBgyjjr14OGCZQDG2NZco6K4OXya6S3d/ZEjO3oSiSmISq9w0qeR0HQy8koiT0cpaXN1U5P0wr49hzQxFlIQbf1dMJRaveWunjVw2abls0sboC0wh2w9fNYvFSz8kVL2WAiHMpIo8Pn/mHO7fmMPD6yLcfMYEvn3Bcdzzhw8tGaW/NE2jdesHVAjNXDBjGHVhgYN7NuJDpo0gKgLXnf0JOg/uZNumNSiIKIhMEA7QQCELfZ/izpuvhuIxugtyP2Dci3S1C11cXAYe/SoaBUEYCdyPnidCAN4Gvq1pWnUW2waA24Hr0HNKrAV+pGna+xk3HKjsXIR/078TX3O2PAMfjIczbyUUkZFQ8A7gcB67sWYwCt1jgXQuoaV5fsLxe2K3NJbnBxLrmbcxf05YGk3bWV1Yk/uVFRWPJKa0YRRvz/eqFLRs5jxxJROEA5y3r5ErffuZIOzH36QQeExkvT9kjV8zEqR2wOR+HGPIUoCNsUpqtSHMn38yj23sYnN7kENaLgEhyl6tgkatkBB6gpitN12Axyux4sVNvLG7ilmlk2G4nt1O3rGLpWqYxvFn85BiTcXdfNxZ3PfqO3xz9ETOGzeJztyVrNa8iRsgSENYKnef+fD7Z0yipr6Bles2UkQHxUIH/3tWKc+8t5LxwgEm5IRQu9ooF1opE1LL/fSWiJSHJIeJBIaQWzqaPXVN5MtNiKgIQKBgCDmj5vGfjYcIqB3E8FCnlZA3dDyfP/9UPZaudBI3PbWZ97Y3UBIQWfU9vQbs/X9ayqrWFv456UQa97WwRNWthNeXTWQ/HRSJ1ji6PL830WejpsLSxqSFUxKkdBhJPIKmCQ/DYuIkRoQszNlmsWnPPO1k+TMfr+HqZ87SGOyFtTCTODtSmL0edUuj/k4x3k+Q2TqYzmJlKVRusTRaLZBOBcSdMLazu9I3dURJUyM8LR0x2COO5iNVtwhfPGQYn585l5bdG+liL/6AnuExz3Yvt0RKWarqtZynTZ/DjoMdPLBjh2WdL5xyAS3NYa5dqw+J/B6RSLwG3ci8HCid0LOD7WMSMY0e19XDxWWw0G+iURCEIPAOuk3gC+jDoTuAdwVBOE7TtFCm7YFHgYuBH6DnCfs68IYgCAs0TVt75I78CLFrUeqyxfdA1RJu2LuEGwIQQ4KHZ8LQGXoAeslYyK+EYEk8WUFxv80aprinDsLkPccCTpl2jWXGIOygzdJYke/Dg0y4szMRgxXuCjNSOMhooZ6RLfsYEvHyGWkLw9s9sFKvczSuqp7veLZRqKpUrnyNX3l2EhQiqM/8GySRYR1RnvRWkSt0kvfYr8Aj8n8NzYxU9uN9IsafjYQyh0hmvgQ9Zq4/xhH5w1CHzebBzX7qtWIkVDrxMXraiSzc2ELQ5+GWy87hpqfWA7D01LN4aftydqvxV5XtIfCIQkrBZPOsujEQtN8PIFEvzFjHPoh32saJjohCa8zPLm144hhvHHciv31bj1cbX5TLrjb9+EtoY2qgETHaEZd0kEcXEgqFPpVvjK1j286djB03kQ27axgl1FNZWkx55Wgom6K/h4KlfOe1elY0+rjvS+ezoynKbc9v5I4LZ3DdiaP59oNLWGeqHfrCZ05m1sgifrNjUaIQN8C5BRV8fuLxie/BgA8VEX8gNVurJAoWoWbUH7N3ofyAh/YuvX+bs/0m4mJ7IJQMEehkkfMcZqmhYIbCz07HZhZATin9e2Mt7A/RaMYsyCoKkpbGTNZBsxUxG8xtFQW9KZNp3W1njruE7J9JM+GobPmttE8OGO8P+70078seI27e1nwfgz4pUR7Ic5RcUDNhiPwstbqLi8sAoD9/GW4ExgGTNU3bCSAIwnpgB3ATcF+6DQVBmAVcA3xR07S/xZctBjYBvwQ+eWQP/QjQsNV5+d4liY9eFKhdq/9Lh78AvEE9gN343xfUP3v8egyD5NUz2hlZ7USvLjZFj+lzfB1BjGfCE5P/LN/1z58RllEutZAvhBkitDFz5RCoKtDjI0SPNZOe0YZluZFtz2PLvCfpqdnzh/YoM9tRJ1CoZxcMllgzEh5lwnHXznKhlYlCDQVCmOCmGjiYy6ltmznJU0ugXSDoaSNfCFNBC/Nf38cVgXZ4Dv0f+gP0SWOsFs9iebUXPQ7wFf37GcAZRgHr7TDOeJvEu/IQ4GTjlul5KHBOtn3kiAgBmtUgHf6h7Mmdxcb6GI0U0qLlccuV57JkXxcvLd/K5afO44sXnYQI3PfjVyxt3FQ8jt3abir8fjymepXdDayDPillMG/+bmzvNFg1lhlWDWPd8nw/9e2RrAe49kEpwCFTIiRzO80UsFopplNNtVbnaR5OnDWDb21ZyxOnnMA3ty0H4A9nzOXi46wp63e+s4T9HMLjC5Af0EeE6VzQkoJN7yhDcn00haKp+7etZ0awxboV5Oif7YWX8/weOuKJocyiMc/B1TRbpD6sRZupru3hCMDeHJvdsnU0MI62LN9PKJJ0Ty3L0j21p26O5r5UHPQ5PlNl+f7EBI79GMJR3T3V/kwa3522NZYZ63REFIu10n5+dvdkY3vzseaZEoJZtxVsotGTyE57uBMbfYlxv7It1+Ti4tL/9Kdo/CSwzBCMAJqm7REE4UPgMjKIxvi2MSDhz6lpmiwIwr+AHwuC4Nc0rZ+jmnrA3y6CvR/2TVuRNv3fUeZue0b1/TimzHc5sowBNgZsC+Nd6xZIPvHmJ793td/7DVWQiBRPZlFDPl25w1ncVkk7QUJagM+fPZdPzhvL/77TylMr93Pu+Ar8HpGXa5OpPr81fBatDQfYoEU5z5++aq8h3PweyeIC9//bu/P4OMo7z+Ofn862Wrdl2cYHNmBjMAZzTEgC4coCOTkWCLCTbCAzSYBkJtmZzc4ymWTIBOZINkvuk8kyL2BzsEnA2c0m4JCD7AsmMYwxmACB2Fg2OD4kIdm61b/946mSSuVuSTayW2p9369XvVr11FPVVf3rbtXTzzVRc8aJbtrj4+7qPrCGIk4bKSxFzzUvutns7ptcTX66NgRg977Rr8b0cfI1b4bQciDO25qs+ckzf2dc45NzHynsFGo6mE3VpNZmKvIWGuPXKtmkMFmxmywAxMdKV77VZSrIebjG5PylcZPHdK3woZqiwSXHmMrC6WTUFKFPY/y6za+vprtvKGrSXjbmXMYboOdgB1TJpmoa85lfn6fQGL1fuqNRpZe11ESFxvCZba0Pn9FsVTm7CxyvOVvFnn39Y5rhhmPnf93j6473T35ua6rKC74uye+oTKJfbcU06EdYqUKjyIxTzELjauD+POmbgasmse8Wd09PT7uZMIvacdHfM8PwDL1rFzmMtubm8weaaPc6chhHLTmWU888n7d+ezd9VPHxd76JuY0NfPALv2J1TT2bO0d/LLm09hhoOpps5mkgfy3EZGsm4hsys7HDw0904zVR96a4QLhxW+cB2+K0dNPJbHUFmcoy+gYnd6O1vaOXvfvGFsLyPd9E3GHT9rBfcmCSfLVzIzeDQ7mR8y40rH68PVMZ9yvM/y9ptJlugZvj5MBKI8dI1TRGBdxfPreHjp6BA/IfjsLeTFXM5qkttdW0tfeyZU8PtdUVY+IyZ5wmvAc7dUPyR4K4SXNac7b6gLT4tXlyeyfuML8uw1N0jXyu4vV8TYbjbdUVZWSrKvj9nn0jTaaTxy70nPNqDzyfirKygp+bQn04p0VNY/T6J3/AEZHprZg/NzUDHXnS24GmV7FvvH3mmM7NLkUOh2wrDw+fxDo7n8dX38yfDPwltx//P1nTdwdr+u7g1Nxd3NB8B1cPfJwbB/8THxj8MM+s/Ws45Wq2Va3gBV/E3MYGWqKbqHNWzuOPlo1+baycH5oIx03bGmoqOXN5+FqI8zXMqeTkxY0ArF5UP+b04pqHtUsaR46xsCEzkh7f8FxwfOuY/Y6dlx35e2lzzcjfpy4Nz7NqwWjT5fi49z62HYCLThyd2TROa6mtGpN3brZqTHM9gNOPHvt1uWbR6KA6v97Szrb2sb+txceejPj5Ab67YTs1VeXUZUZrZfLdxJ6zMtTattZnWNAQqr3nRseJt8WvR3yzGz/PGVFs4liNPE90zcmb+7OOawFgQX1mZHumsmykcHjR6rEzxcbnesPdj/HC7tEu83Gz0HjAldcknnvtksYxx3jNsrDtpEVjBy6qqSrnlCjvCQvHvpcATk+8N5NxzidZI/T6Y8M1HtV4YF/OQg51KgmAN58UmhofzKBAU+XC6HVZ3pLlld5B1v/2D7TUVo/8SPGGFfNGCmKvWX7gv/iKMuO1x8wdkxa/loubDnz9zGykBnfF/PxTk8Sf5zeeMPo5r6kqJ1tVznc3hM9RHO/4cxWvL6jPsKB+bLOPeFtLbTXz6qr50ZM7x4wyHb+PT1sa3i/HR98X8Xu3tS4z5jMJ0JStHNmvsaYyb4HwpEX1XLBq9BrOWdmS93qPpPh7a8X84nXnEJGDY/mG6D8iT2w2AHzG3W9Opd9GGAW14E+dZvYgUOvur0ulXwg8AJzj7g/n2e99wPsAli5devqLL05+iPXDKk/z1F3HXsGeo99K40u/INvxDNU19exc+EZ6q5rJ7NtGpnsb1fvaqOxrp2Kgg8r+TioGjnyzVCkNbmV4+jekigw+bxVdHm64MrVNdFl2zAiBtfWNdFBPLufUZbN0VzbTl/jluD5bQ2+mlX1RE6wyM1Yct4LqpsXs3T9AVUUZtdUVtLX3clRjho1tnQwM51jYMGekmdr2jl5y7qxd0khleRk7OnvZu6+fNYsaMDPa2ns4qnEOA0M5uvsG6RvMsXRuKLD1Dw3zRNsrnLCwbszz7OzqY3FTyLNtb89IfoC9+/qprCijb2CYukwlleXGxrZOjplXS1NNJU/t6KIpW8niphqGhnPs7Oqjq3eImqryqD/WEG0dvSxtrhlTwEs/j7vz1I4uuvsHqc9UsmpBHTu7+ujuG6KjZ4BsVQUnLw7XODicY2NbJytb6+juH2Rbew/L5mYZzjnz6zPs2dfP4HCOOZWhUNfRM0CZGb/fsw+A41prwaGto5f+odAPq38oR1fvEMe2ZqNtPbjD0rk1GIa7M6eqnN7BYbbt7RmJy/KWLNv29tDdP8jqo8YWnuLr2t7Ry5Lm0dd3SfMczIzhnPNSZy+t9dW07x9gYUN4b73SM8hzu7pZu6SRlzv7RvLHBobC9R+/oI6GOaHQGh9rSXPNyGsZx2VHZy+tddVjap+Gc87Gtk76h8J78bh5oaDQmrixj99LOzp6yVSWUT+ncuT9N6eynEWNc2jr6OHouaM/DrTvH6Ci3KjPVPLi3v1jtiVfk7b2XirKjZba6oJNKZPHAsjlnB2do69lWmfPAIbRkGhe2dU3yNCw05zNX3s2nv6h4TFxOZIGh3Ps6u5nbraKjW2d5NxZ3pJlYcOckbiUlxm7uvqoy1Qyp6qczp4BBoed4ZyzoCEz8jqXlYXm5NWVZdRnKtnfP0Tv4PDIj0yxtvYeXukdZNWCOja2dbKkuYate/azvCXLlj37OWVJI3v3DxzwXtq6Zz8vvdJLVXkZa5c08szObrr6Rj/H8ffFwFD4fmiqCSP3rpwftq1oraVnYJite/dTZhY+U+09nLqkcaR2MPl9kfwe6+wZpK2jh+Ut4fO/uCm8/5/c8QottdVkqyvI5ZymKP67uvuora4gU1HOjs5ezGBhw5wj3uQ5n/R3oogUh5k95u5nTJiviIXGPwD3ufv7U+lfBq5y94KdjMzsO8Badz8+lf4OQj/Hk9x93OapZ5xxhm/YsOGQz39KJQuNlTWw8mJ4++fC4CoHY3go9Gcc6oPBXhjsST32Qm4oLMODkBsM++QGo7To73hbbhg8F5bk3z4c2qsl0yFMEFyVDY9lFdFzDYf8ueHE30Oj+8V/54aibbnE31F6bwf0Tf10AFPGc9D9Mgz2wb6do69HsVhZGFW3YRE0HxPWY9kWqMyGAZDqFoSBklpXw9xjVeMtIiIiMstMttBYzD6Nmwl9E9NOBJ6exL6Xm1lNql/jicAA8Hz+3aap68aO1njInWvKK8LonVI8RfoR5gDqoCUiIiIiU6SYfRrXAa81s5FR+M1sGXBWtG2ifStJDJhjZhXA1cADM2rkVAg3+MlFZq50LIu1iIiIiIhMkWIWGr8BbAXuN7NLzewSwmiqbcDX4kxmdrSZDZnZx+M0d99IaIb6WTP7UzN7I/BtYDnwt0fwGkREREREREpa0QqN7r4fuAB4DrgLuAfYAlzg7vsSWQ0o58BzvR74H8CthOnGlwBvcvfHD/Opi4iIiIiIzBrF7NOIu28Drpggz1bSE26F9F7gL6JFREREREREDoNiNk8VERERERGRaU6FRhERERERESlIhUYREREREREpSIVGERERERERKUiFRhERERERESlIhUYREREREREpSIVGERERERERKUiFRhERERERESlIhUYREREREREpSIVGERERERERKcjcvdjnUBRmtht4sdjnkdIC7Cn2SUhRKPazl2I/Oynus5diP3sp9rPTdI/70e4+b6JMs7bQOB2Z2QZ3P6PY5yFHnmI/eyn2s5PiPnsp9rOXYj87lUrc1TxVREREREREClKhUURERERERApSoXF6+XqxT0CKRrGfvRT72Ulxn70U+9lLsZ+dSiLu6tMoIiIiIiIiBammUURERERERApSobHIzGyJmf0vM3vFzLrM7PtmtrTY5yWHxsyuNLPvmdmLZtZrZs+a2T+YWV0qX5OZ3WFme8xsv5mtN7M1eY6XMbNPm9nL0fEeMbNzjtwVyaEysx+bmZvZral0xb4EmdlbzOyXZrYv+i7fYGYXJLYr7iXIzM4yswfMbFcU98fN7D2pPJOKqZmVmdnNZrbVzPrM7Akzu+LIXY3kY2aLzewLUdx6ou/1ZXnyTXmczey9ZvaMmfVH9xM3TP0VSiGTib2ZnWFmX4/i1GNm28zsHjNbnud4Mzr2KjQWkZnVAA8Bq4B3A+8CVgA/M7NsMc9NDtl/BoaBvwbeBHwFuBF40MzKAMzMgHXR9j8DrgAqCXFfnDrePwPvBT4OvA14GfiJma09/Jcih8rMrgVOyZOu2JcgM3s/cD/wGHA5cBVwL1ATbVfcS5CZnQysJ8TyvYS4/gb4ZzO7MZF1sjH9JHAL8EXgzcCjwL1m9pbDeBkyseOAdwAdwMPj5JvSOJvZe4GvAd8jfHfcC3w59d6Sw2sysb8GWA18nhDP/wqcBmwwsyWpvDM79u6upUgL8CFCAeO4RNpyYAj4i2Kfn5ZDium8PGn/EXDggmj90mj9/ESeBqAd+Hwi7ZQo3/WJtArgWWBdsa9VS8H3QCOwE7g2it+tiW2KfYktwDKgF/jwOHkU9xJcgL8HBoDaVPqjwCMHE1OgFegHPsPmPdIAAAuoSURBVJE61k+BTcW+1tm8AGWJv/80iueyVJ4pjXO07y7gX1L5vkmYJL6y2K/LbFgmGft8931HAzng70op9qppLK5LgEfd/fk4wd23AP+PcJMhM4y7786T/JvocVH0eAnwkrv/LLHfK8APGRv3S4BB4DuJfEPAt4GLzax6Ck9dps6ngM3u/q082xT70vMews3BV8fJo7iXpipCvHpT6Z2MtuSabEwvjo53d+pYdwNr8jV1kyPD3XOTyDbVcX4dMC9PvruAucDZB3MNcmgmE/t8933u/iKwm9H7PiiB2KvQWFyrgafypG8GTjzC5yKHz7nR42+jx/HivtTMahP5trh7T558VYRmEzKNmNnZhJrlmwpkUexLz9nAM8A1ZvaCmQ2Z2fNm9oFEHsW9NN0ZPX7ezI4ys8aoWdkbgdujbZON6WpCLcTzefKB7gmmu6mO8+roMf29offDDGBmJxBqFn+bSJ7xsVehsbiaCe2k09qBpiN8LnIYmNki4O+A9e6+IUoeL+4wGvuJ8jVP1XnKq2dmlYQ+CP/N3Z8tkE2xLz1HEfqifxr4R+Ai4EHgi2b2oSiP4l6C3P0p4DxCbfEOQuy+BNzg7t+Osk02ps1Ap0ft0MbJJ9PTVMc5fkwfU++Hac7MKggtT3YT+rnGZnzsK4r1xDIi30SZdsTPQqZcVHtwP6GP6vXJTUwu7pPNJ9PDXwFzgNvGyaPYl54yoA64zt2/H6U9FI2wd7OZfR7FvSSZ2QrCQBWbgRsIzVQvBb5qZn3ufg+K/Wwx1XGO1zWZ+szzReD1wFvdPVnwm/GxV6GxuDrI/4tBE/l/sZIZwswyhNESjwHOdfftic3tFI47jMa+Hcg3/UpTYrtMAxamyfkooaN8darvWbWZNQLdKPalaC+hpvHBVPoDhFHvFqK4l6q/J/Rje5u7D0ZpPzWzucDnzOxbTD6m7UCTmVmqJkKxnxmmOs7JWqWXE/maU9tlGjGzfwDeB7zb3R9IbZ7xsVfz1OLazGjb5aQTgaeP8LnIFImaKX4PeA3wFnd/MpVlvLhvc/d9iXzLo6lZ0vkGOLBdvBTPMUCG0HG9I7FAmIalA1iDYl+KNhdIj38tzqG4l6o1wBOJAmPs14QBK1qZfEw3A9XAsXnyge4JprupjnP8vZL+3tD7YZoys48Sptv4kLvflSfLjI+9Co3FtQ54rZkdEydETZrOirbJDBPNxXgPYSCES9390TzZ1gGLzOzcxH71wNsZG/d1hPm/rkrkqwCuBh5w9/6pvwI5RBuB8/MsEAqS5xNuGhT70vOD6PHiVPrFwHZ334niXqp2AmvNrCqVfibQR6gRmGxMf0woXPxx6ljvBJ6KRlaX6Wuq4/wIYXqFfPnaCaPsyzRhZn8O3Ap81N2/UCDbjI+9mqcW1zeADwL3m9nfENovfxJoIwyoITPPlwj/NG4D9pvZaxPbtkfNVNcRvhTuNrOPEGqhbibUTHwqzuzuG83sO8Bno9rLLcCNhLk8018mUkTu3gn8PJ0e5nTnRXf/ebSu2JeeHwE/A75mZi3A74ErCQPixH2ZFffS9EXCpNs/NLMvE/o0XkKYo/V2dx8AJhVTd99lZrcT+sF2A48TChwXoCm4is7Mroz+PD16fLOZ7QZ2u/svJvvZnWyc3X3QzD5GmNB9B7A+yvMe4M+i95YcARPF3syuAT5LKBQ+lLrv63L3p6FEYl+sCSK1jEzWuZTQlLGL0OfpPlITh2qZOQuwlVD4z7fcksjXTJiotR3oIUzuekqe480B/jvhF+0+4F+B84p9nVom/X5w4NZUmmJfYgtQT/jB6A+EX5I3Af9BcS/9BXgz4Qej3dH/8I2EKXfKDzamQDnwN8CLhKH5NwFXFvsatYx8l+dbfn444wy8H3guyvc74KZivxazbZko9oSpdyZ8f5RC7C06MREREREREZEDqE+jiIiIiIiIFKRCo4iIiIiIiBSkQqOIiIiIiIgUpEKjiIiIiIiIFKRCo4iIiIiIiBSkQqOIiIiIiIgUpEKjiIjIOMzsFjNzM1tWpOe/08w0P5aIiBSNCo0iIjLjmdl5UcGu0DJU7HOc7szstOi1el20fma0fnKxz01ERIqrotgnICIiMoW+BfwoT3ruVRzzVuAfgf5XcYyZ4PWEa3wsWj8b6AKeKtoZiYjItKBCo4iIlJLH3f3uqTyguw8Bs6Gm8vXAb9x9IFo/G/hXd381BW4RESkBap4qIiKzipkti5pd3mJm15rZJjPrM7NtUVpFKv8BfRrNrNnMbjezF6J995rZY2b2kdS+FWb2V2b2dCLfD8xsTZ7zypjZp83sJTPrNbNfm9lF41zHCjO7y8xeNrMBM9sa7Z+d5OtQZmYt8UIoND6RWD8L2JTIUzmZ44qISOlRTaOIiJSSmqjAkzbg7l2ptLcDHwa+BOwELgH+FjgauH6C57kXOAf4GvAEUAOsAs4DPp3Idw/wDuBB4CvAAuADwCNm9gZ3/7dE3m8BlwE/BH4CHAt8H9iSfnIzOx14COiMzmEHcArw58BZZnauuw9OcA1L8xz7A9ES+8toATgf+PkExxQRkRJk7hqQTUREZjYzOw/42ThZ/o+7vy3Ku4xQWMoBf+Tuj0fpRiikXQa8zt0fjdJvIRQml7v7VjNrIBTWvuLuN41zThcCDwDfBa7x6B9uNLDM48Aj7v6GKO0iQkHxX9z9usQxLgN+AODulkh/AqiOzr87kX55dA3Xu/ud47wemFmG0AQVQgH6JkLBeRC4ArguWo9vFB5z947xjikiIqVJNY0iIlJKvk6oBUzbnSftwbjACODubmafIhQaLwceLfAcvYQBY840s2XuvrVAvsujx9s88Qutu28ys/8NXGpm89x9d/ScMLaWEne/z8yeBY6P06KmrScTCrLVZlad2OVXwH7gIuDOAucVH7sPWB8d808I/Rf/b7R+E/Cwuz843jFERGR2UKFRRERKye/cff0k8/42T9rT0eMxhXZy9wEz+zDwOWCLmT1NaCp6n7v/NJF1OaE2M9/zPAVcGuXZHT1fDniuwHken1g/IXr8RLTkM7/Q+UPozwg0x6vAhcA3o6a9RmiK+oVEU992DYgjIjJ7qdAoIiKz1SH3z3D3r5rZ/cBbgXOBK4EPmtl33P2aKJsVPMCBxsub3havfwb4cYF9JmpGmq8/40eiJfaxaIFQuN06wTFFRKREqdAoIiKz1YnjpP1+op3d/WXgDuAOMysH7gKuNbPPuPtvgBeAiwk1g5sKPE9ccHuB0KR0JbA5lXdVav130ePwQdSqpu0k1C5CKPC+k9Emsu8i9GW8KpVfRERmKU25ISIis9WFZnZavBINhPNfotX7Cu1kZjVmVpNMc/dhRguGcbPP+Bg3R8eO9z+JUCj7VdSfEeD+6DE9ZcdljG2aCvBvhOatN5jZAc1oo2k+mtPpqfPtc/f1UaGzmTAoT7zeEp3b+sTSN97xRESktKmmUURESslpZvbOAtvuc/d9ifUngIfM7EvAy4Q+hv8OuMvdHxnnOVYCvzCzHxAKbx2E2sQbCTWHDwO4+4Nm9l3gGqApGvwmnnKjjzA9BlHen5jZD4F3RwW+HxOm3Hh/9BwnJfK6mb2L0I9yk5l9k1A7WQMcB/x74GYmGAgn4RzCtCNxX8ezgNsmua+IiMwCKjSKiEgpuTZa8lkBPJ9YXwc8SyhgHQ/sAj4ZLeNpA75JGCzmMsLUFzuAbwD/5O49ibx/TJhe4zpCH8T9wC+Aj7n7k6njXg3cGu1zIaGweEV0PSclM7r7RjM7NTr3S4AbgG5Cv8M7geSAPAWZ2SrCoDm/jJJOARoS6yIiIpqnUUREZpfEPI2fcPdbinoyIiIiM4D6NIqIiIiIiEhBKjSKiIiIiIhIQSo0ioiIiIiISEHq0ygiIiIiIiIFqaZRREREREREClKhUURERERERApSoVFEREREREQKUqFRREREREREClKhUURERERERApSoVFEREREREQK+v8SuEhEp5SCHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6117c97940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "matplotlib.rcParams['xtick.labelsize'] = 16\n",
    "matplotlib.rcParams['ytick.labelsize'] = 16\n",
    "\n",
    "fig = plt.figure(figsize=(15,7))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(np.arange(1, len(scores)+1), scores, label='Episode Scores')\n",
    "plt.plot(np.arange(1, len(scores)+1), scores_avg,\\\n",
    "         linewidth=5,label='Avg. score of last 100 episodes')\n",
    "plt.ylabel('Score', fontsize=18)\n",
    "plt.xlabel('Episode #', fontsize=18)\n",
    "ax.legend(fontsize=14)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
